{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9988ed04",
   "metadata": {},
   "source": [
    "# Prune Dataset to extract representative subset\n",
    "\n",
    "In this notebook example, we will demonstrate the process of creating a pruned dataset, which is a representative subset of the original dataset. By working with a smaller, yet representative, subset of the training data, we can observe how the model's performance is affected in terms of accuracy and convergence speed. Through this, we can check accuracy drop and convergence time with subset of training data through [OpenVINO™ Training Extensions](https://github.com/openvinotoolkit/training_extensions). This analysis provides valuable insights into the relationship between dataset size and model performance. It helps us understand the scalability and generalization capabilities of the model, shedding light on the efficiency of training algorithms and the potential benefits of working with a pruned dataset. It also allows us to assess the impact of dataset size on model performance, providing guidance for resource allocation and model development in practical scenarios.\n",
    "\n",
    "## Prerequisite \n",
    "### Download Caltech-101 dataset\n",
    "This is [a download link for caltech101 dataset in Kaggle](https://www.kaggle.com/datasets/imbikramsaha/caltech-101?resource=download). Please download using this link and extract to your workspace directory. Then, you will have a `caltech-101` directory with images in imagenet format as follows.\n",
    "```bash\n",
    "caltech-101\n",
    "├── accordion\n",
    "│   ├── image_0001.jpg\n",
    "│   ├── image_0002.jpg\n",
    "│   ├── ...\n",
    "├── airplanes\n",
    "│   ├── image_0001.jpg\n",
    "│   ├── image_0002.jpg\n",
    "│   ├── ...\n",
    "│ ...\n",
    "└── yin_yang\n",
    "    ├── image_0001.jpg\n",
    "    ├── image_0002.jpg\n",
    "    ├── ...\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "db31c638",
   "metadata": {},
   "source": [
    "### Install OpenVINO™ Training Extensions\n",
    "For more details, please see this [OpenVINO™ Training Extensions installation guide](https://openvinotoolkit.github.io/training_extensions/latest/guide/get_started/quick_start_guide/installation.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c209d88",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install otx"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b8269823",
   "metadata": {},
   "source": [
    "## Prune dataset using Datumaro Python API\n",
    "\n",
    "In this section, we utilize the Dataumaro Python API to prune entire dataset. We import the caltech-101 dataset and apply dataset prune."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff87df2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-08 01:40:37.166610: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-07-08 01:40:37.725001: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Dataset\n",
       "\tsize=9144\n",
       "\tsource_path=caltech-101\n",
       "\tmedia_type=<class 'datumaro.components.media.Image'>\n",
       "\tannotated_items_count=9144\n",
       "\tannotations_count=9144\n",
       "subsets\n",
       "\tdefault: # of items=9144, # of annotated items=9144, # of annotations=9144, annotation types=['label']\n",
       "infos\n",
       "\tcategories\n",
       "\tlabel: ['BACKGROUND_Google', 'Faces', 'Faces_easy', 'Leopards', 'Motorbikes', 'accordion', 'airplanes', 'anchor', 'ant', 'barrel', 'bass', 'beaver', 'binocular', 'bonsai', 'brain', 'brontosaurus', 'buddha', 'butterfly', 'camera', 'cannon', 'car_side', 'ceiling_fan', 'cellphone', 'chair', 'chandelier', 'cougar_body', 'cougar_face', 'crab', 'crayfish', 'crocodile', 'crocodile_head', 'cup', 'dalmatian', 'dollar_bill', 'dolphin', 'dragonfly', 'electric_guitar', 'elephant', 'emu', 'euphonium', 'ewer', 'ferry', 'flamingo', 'flamingo_head', 'garfield', 'gerenuk', 'gramophone', 'grand_piano', 'hawksbill', 'headphone', 'hedgehog', 'helicopter', 'ibis', 'inline_skate', 'joshua_tree', 'kangaroo', 'ketch', 'lamp', 'laptop', 'llama', 'lobster', 'lotus', 'mandolin', 'mayfly', 'menorah', 'metronome', 'minaret', 'nautilus', 'octopus', 'okapi', 'pagoda', 'panda', 'pigeon', 'pizza', 'platypus', 'pyramid', 'revolver', 'rhino', 'rooster', 'saxophone', 'schooner', 'scissors', 'scorpion', 'sea_horse', 'snoopy', 'soccer_ball', 'stapler', 'starfish', 'stegosaurus', 'stop_sign', 'strawberry', 'sunflower', 'tick', 'trilobite', 'umbrella', 'watch', 'water_lilly', 'wheelchair', 'wild_cat', 'windsor_chair', 'wrench', 'yin_yang']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datumaro as dm\n",
    "from datumaro.components.algorithms.hash_key_inference.prune import Prune\n",
    "\n",
    "dataset = dm.Dataset.import_from(\"caltech-101\", format=\"imagenet\")\n",
    "dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fc57a21d",
   "metadata": {},
   "source": [
    "### Generate the validation report\n",
    "\n",
    "Before pruning entire dataset, we first generate the validation report of dataset to confirm the statistics of dataset labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "680a5c05",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datumaro.plugins.validators import ClassificationValidator\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "validator = ClassificationValidator()\n",
    "reports = validator.validate(dataset)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a3b3205d",
   "metadata": {},
   "source": [
    "The label distribution in the entire dataset is as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cc2ebecb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats = reports[\"statistics\"]\n",
    "\n",
    "label_stats = stats[\"label_distribution\"][\"defined_labels\"]\n",
    "label_name, label_counts = zip(*[(k, v) for k, v in label_stats.items()])\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.hist(label_name, weights=label_counts, bins=len(label_name))\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "75c55551",
   "metadata": {},
   "source": [
    "### Prune dataset\n",
    "We support various prune methods in [Datumaro](https://github.com/openvinotoolkit/datumaro). We will try each method and compare the results by examining the train and validation reports. We will start by checking using the `random` method. For all methods, we will use a ratio of `0.5`."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0a4bef84",
   "metadata": {},
   "source": [
    "The random method involves randomly selecting dataset items from the entire dataset to create a subset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f768c7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "prune = Prune(dataset, cluster_method=\"random\")\n",
    "random_result = prune.get_pruned(0.5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "612bbd8b",
   "metadata": {},
   "source": [
    "When creating a subset using the random method, as shown below, we can observe that the label distribution changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4dac0a1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_reports = validator.validate(random_result)\n",
    "\n",
    "random_stats = random_reports[\"statistics\"]\n",
    "\n",
    "label_stats = random_stats[\"label_distribution\"][\"defined_labels\"]\n",
    "label_name, label_counts = zip(*[(k, v) for k, v in label_stats.items()])\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.hist(label_name, weights=label_counts, bins=len(label_name))\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b41de300",
   "metadata": {},
   "source": [
    "In this case, we use `cluster_random` method. For detail information about each method, please refer [prune](https://openvinotoolkit.github.io/datumaro/latest/docs/command-reference/context_free/prune)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d7c6f2d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "prune = Prune(dataset, cluster_method=\"cluster_random\")\n",
    "cluster_random_result = prune.get_pruned(0.5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d8a40abb",
   "metadata": {},
   "source": [
    "When creating a subset using the cluster random method, as shown below, we can observe that the label distribution changes. In the case of this dataset, the distribution of each class did not change significantly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "49872bb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_random_reports = validator.validate(cluster_random_result)\n",
    "\n",
    "cluster_random_stats = cluster_random_reports[\"statistics\"]\n",
    "\n",
    "label_stats = cluster_random_stats[\"label_distribution\"][\"defined_labels\"]\n",
    "label_name, label_counts = zip(*[(k, v) for k, v in label_stats.items()])\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.hist(label_name, weights=label_counts, bins=len(label_name))\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9294ef04",
   "metadata": {},
   "source": [
    "We use `query_clust` method. For detail information about each method, please refer [prune](https://openvinotoolkit.github.io/datumaro/latest/docs/command-reference/context_free/prune)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b87ee075",
   "metadata": {},
   "outputs": [],
   "source": [
    "prune = Prune(dataset, cluster_method=\"query_clust\")\n",
    "query_clust_result = prune.get_pruned(0.5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bd547e06",
   "metadata": {},
   "source": [
    "When creating a subset using the query clust method, as shown below, we can observe that the label distribution changes. In the caltech-101 dataset, when the datasets included in each class were small, there is a tendency for the ratio of classes with more data to increase compared to the datasets of those classes. This tendency may vary for different datasets, so we recommend comparing different methods directly on your own data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "444e92de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query_clust_reports = validator.validate(query_clust_result)\n",
    "\n",
    "query_clust_stats = query_clust_reports[\"statistics\"]\n",
    "\n",
    "label_stats = query_clust_stats[\"label_distribution\"][\"defined_labels\"]\n",
    "label_name, label_counts = zip(*[(k, v) for k, v in label_stats.items()])\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.hist(label_name, weights=label_counts, bins=len(label_name))\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d4a840ce",
   "metadata": {},
   "source": [
    "We use `centroid` method. For detail information about each method, please refer [prune](https://openvinotoolkit.github.io/datumaro/latest/docs/command-reference/context_free/prune)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aca13c0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "prune = Prune(dataset, cluster_method=\"centroid\")\n",
    "centroid_result = prune.get_pruned(0.5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d42bbd0b",
   "metadata": {},
   "source": [
    "When creating a subset using the centroid method, as shown below, we can observe that the label distribution changes. In this case, we can see that the proportion of motorbike, which had a large amount of dataset, has decreased. This illustrates the tendency of certain classes to have a reduced proportion of their data within the overall dataset. It is possible to identify such trends where the contribution of data from specific classes decreases within the entire dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0ba59505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "centroid_reports = validator.validate(centroid_result)\n",
    "\n",
    "centroid_stats = centroid_reports[\"statistics\"]\n",
    "\n",
    "label_stats = centroid_stats[\"label_distribution\"][\"defined_labels\"]\n",
    "label_name, label_counts = zip(*[(k, v) for k, v in label_stats.items()])\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.hist(label_name, weights=label_counts, bins=len(label_name))\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "483914c2",
   "metadata": {},
   "source": [
    "We use `entropy` method. For detail information about each method, please refer [prune](https://openvinotoolkit.github.io/datumaro/latest/docs/command-reference/context_free/prune)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc889f6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "prune = Prune(dataset, cluster_method=\"entropy\")\n",
    "entropy_result = prune.get_pruned(0.5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5323b439",
   "metadata": {},
   "source": [
    "When creating a subset using the entropy method, as shown below, we can observe that the label distribution changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d30f8181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "entropy_reports = validator.validate(entropy_result)\n",
    "\n",
    "entropy_stats = entropy_reports[\"statistics\"]\n",
    "\n",
    "label_stats = entropy_stats[\"label_distribution\"][\"defined_labels\"]\n",
    "label_name, label_counts = zip(*[(k, v) for k, v in label_stats.items()])\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.hist(label_name, weights=label_counts, bins=len(label_name))\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8e4b2bbc",
   "metadata": {},
   "source": [
    "Finally, we use `ndr` method. For detail information about each method, please refer [prune](https://openvinotoolkit.github.io/datumaro/latest/docs/command-reference/context_free/prune)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "38f5855d",
   "metadata": {},
   "outputs": [],
   "source": [
    "prune = Prune(dataset, cluster_method=\"ndr\")\n",
    "ndr_result = prune.get_pruned(0.5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "734f42eb",
   "metadata": {},
   "source": [
    "When creating a subset using the ndr method, as shown below, we can observe that the label distribution changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "01f61373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndr_reports = validator.validate(ndr_result)\n",
    "\n",
    "ndr_stats = ndr_reports[\"statistics\"]\n",
    "\n",
    "label_stats = ndr_stats[\"label_distribution\"][\"defined_labels\"]\n",
    "label_name, label_counts = zip(*[(k, v) for k, v in label_stats.items()])\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.hist(label_name, weights=label_counts, bins=len(label_name))\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c9b2d6ad",
   "metadata": {},
   "source": [
    "We export those pruned subset to train the model with OpenVINO™ Training Extensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "98035fcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_result.export(\"random_result\", format=\"datumaro\", save_media=True)\n",
    "cluster_random_result.export(\"cluster_random_result\", format=\"datumaro\", save_media=True)\n",
    "query_clust_result.export(\"query_clust_result\", format=\"datumaro\", save_media=True)\n",
    "centroid_result.export(\"centroid_result\", format=\"datumaro\", save_media=True)\n",
    "entropy_result.export(\"entropy_result\", format=\"datumaro\", save_media=True)\n",
    "ndr_result.export(\"ndr_result\", format=\"datumaro\", save_media=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "745395cc",
   "metadata": {},
   "source": [
    "## Train Model and Export the Trained Classification Model Using OpenVINO™ Training Extensions\n",
    "\n",
    "In this step, we train a classification model using OTX. To see the detail guides for OpenVINO™ Training Extensions usage, please see [How-To-Train](https://openvinotoolkit.github.io/training_extensions/latest/guide/tutorials/base/how_to_train/classification.html). In this example, we use the CLI command to train the model and choose `EfficientNet-B0` model supported by OpenVINO™ Training Extensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba8c6fdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*] Workspace Path: otx-workspace-CLASSIFICATION\n",
      "[*] Load Model Template ID: Custom_Image_Classification_EfficinetNet-B0\n",
      "[*] Load Model Name: EfficientNet-B0\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details.\n",
      "  warnings.warn(\n",
      "2023-07-10 18:25:50,278 | WARNING : Duplicate key is detected among bases [{'model'}]\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/model.py\n",
      "2023-07-10 18:25:50,307 | WARNING : Duplicate key is detected among bases [{'model'}]\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/model_multilabel.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/data_pipeline.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/deployment.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/hpo_config.yaml\n",
      "2023-07-10 18:25:50,407 | WARNING : Duplicate key is detected among bases [{'model'}]\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/model_hierarchical.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/compression_config.json\n",
      "[*] Update data configuration file to: otx-workspace-CLASSIFICATION/data.yaml\n",
      "2023-07-10 18:25:50.679934: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-07-10 18:25:51.257037: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/site-packages/openvino/pyopenvino/__init__.py:10: FutureWarning: The module is private and following namespace `pyopenvino` will be removed in the future\n",
      "  warnings.warn(message=\"The module is private and following namespace \" \"`pyopenvino` will be removed in the future\", category=FutureWarning)\n",
      "2023-07-10 18:25:54,091 | INFO : Classification mode: multiclass\n",
      "2023-07-10 18:25:54,091 | INFO : train()\n",
      "2023-07-10 18:25:54,104 | INFO : Training seed was set to 5 w/ deterministic=False.\n",
      "2023-07-10 18:25:54,106 | INFO : Try to create a 0 size memory pool.\n",
      "2023-07-10 18:25:55,390 | INFO : configure!: training=True\n",
      "2023-07-10 18:25:55,504 | INFO : init weight - https://github.com/osmr/imgclsmob/releases/download/v0.0.364/efficientnet_b0-0752-0e386130.pth.zip\n",
      "2023-07-10 18:25:55,527 | INFO : 'in_channels' config in model.head is updated from -1 to 1280\n",
      "2023-07-10 18:25:55,528 | INFO : configure_data()\n",
      "2023-07-10 18:25:55,528 | INFO : task config!!!!: training=True\n",
      "2023-07-10 18:25:55,528 | INFO : train!\n",
      "2023-07-10 18:25:55,528 | INFO : cfg.gpu_ids = range(0, 1), distributed = False\n",
      "2023-07-10 18:25:55,547 | INFO : Environment info:\n",
      "------------------------------------------------------------\n",
      "sys.platform: linux\n",
      "Python: 3.10.0 (default, Mar  3 2022, 09:58:08) [GCC 7.5.0]\n",
      "CUDA available: True\n",
      "GPU 0,1: GeForce RTX 3090\n",
      "CUDA_HOME: /usr/local/cuda\n",
      "NVCC: Cuda compilation tools, release 11.1, V11.1.74\n",
      "GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
      "PyTorch: 1.13.1+cu117\n",
      "PyTorch compiling details: PyTorch built with:\n",
      "  - GCC 9.3\n",
      "  - C++ Version: 201402\n",
      "  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n",
      "  - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\n",
      "  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n",
      "  - LAPACK is enabled (usually provided by MKL)\n",
      "  - NNPACK is enabled\n",
      "  - CPU capability usage: AVX2\n",
      "  - CUDA Runtime 11.7\n",
      "  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n",
      "  - CuDNN 8.5\n",
      "  - Magma 2.6.1\n",
      "  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n",
      "\n",
      "TorchVision: 0.14.1+cu117\n",
      "OpenCV: 4.7.0\n",
      "MMCV: 1.7.1\n",
      "MMCV Compiler: GCC 7.5\n",
      "MMCV CUDA Compiler: 11.1\n",
      "MMClassification: 0.25.0+c5ac764\n",
      "------------------------------------------------------------\n",
      "\n",
      "2023-07-10 18:25:55,910 | INFO : init weight - https://github.com/osmr/imgclsmob/releases/download/v0.0.364/efficientnet_b0-0752-0e386130.pth.zip\n",
      "2023-07-10 18:25:55,919 - mmcv - INFO - initialize CustomLinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01}\n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.init_block.conv.conv.weight - torch.Size([32, 3, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.init_block.conv.bn.weight - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.init_block.conv.bn.bias - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.dw_conv.conv.weight - torch.Size([32, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.dw_conv.bn.weight - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.dw_conv.bn.bias - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv1.weight - torch.Size([8, 32, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv1.bias - torch.Size([8]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv2.weight - torch.Size([32, 8, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv2.bias - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.pw_conv.conv.weight - torch.Size([16, 32, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.pw_conv.bn.weight - torch.Size([16]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.pw_conv.bn.bias - torch.Size([16]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv1.conv.weight - torch.Size([96, 16, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv1.bn.weight - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv1.bn.bias - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv2.conv.weight - torch.Size([96, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv2.bn.weight - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv2.bn.bias - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv1.weight - torch.Size([4, 96, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv1.bias - torch.Size([4]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv2.weight - torch.Size([96, 4, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv2.bias - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv3.conv.weight - torch.Size([24, 96, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv3.bn.weight - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv3.bn.bias - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv1.conv.weight - torch.Size([144, 24, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv1.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv1.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv2.conv.weight - torch.Size([144, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv2.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv2.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv1.weight - torch.Size([6, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv1.bias - torch.Size([6]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv2.weight - torch.Size([144, 6, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv2.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv3.conv.weight - torch.Size([24, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv3.bn.weight - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv3.bn.bias - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv1.conv.weight - torch.Size([144, 24, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv1.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv1.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv2.conv.weight - torch.Size([144, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv2.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv2.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv1.weight - torch.Size([6, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv1.bias - torch.Size([6]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv2.weight - torch.Size([144, 6, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv2.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv3.conv.weight - torch.Size([40, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv3.bn.weight - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv3.bn.bias - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv1.conv.weight - torch.Size([240, 40, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv1.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv1.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv2.conv.weight - torch.Size([240, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv2.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv2.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv1.weight - torch.Size([10, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv1.bias - torch.Size([10]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv2.weight - torch.Size([240, 10, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv2.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv3.conv.weight - torch.Size([40, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv3.bn.weight - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv3.bn.bias - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv1.conv.weight - torch.Size([240, 40, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv1.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv1.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv2.conv.weight - torch.Size([240, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv2.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv2.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv1.weight - torch.Size([10, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv1.bias - torch.Size([10]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv2.weight - torch.Size([240, 10, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv2.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv3.conv.weight - torch.Size([80, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv3.bn.weight - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv3.bn.bias - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv1.conv.weight - torch.Size([480, 80, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv1.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv1.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv2.conv.weight - torch.Size([480, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv2.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv2.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv1.weight - torch.Size([20, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv1.bias - torch.Size([20]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv2.weight - torch.Size([480, 20, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv2.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv3.conv.weight - torch.Size([80, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv3.bn.weight - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv3.bn.bias - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv1.conv.weight - torch.Size([480, 80, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv1.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv1.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv2.conv.weight - torch.Size([480, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv2.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv2.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv1.weight - torch.Size([20, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv1.bias - torch.Size([20]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv2.weight - torch.Size([480, 20, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv2.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv3.conv.weight - torch.Size([80, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv3.bn.weight - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv3.bn.bias - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv1.conv.weight - torch.Size([480, 80, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv1.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv1.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv2.conv.weight - torch.Size([480, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv2.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv2.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv1.weight - torch.Size([20, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv1.bias - torch.Size([20]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv2.weight - torch.Size([480, 20, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv2.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv3.conv.weight - torch.Size([112, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv3.bn.weight - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv3.bn.bias - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv1.conv.weight - torch.Size([672, 112, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv1.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv1.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv2.conv.weight - torch.Size([672, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv2.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv2.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv1.weight - torch.Size([28, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv1.bias - torch.Size([28]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv2.weight - torch.Size([672, 28, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv2.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv3.conv.weight - torch.Size([112, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv3.bn.weight - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv3.bn.bias - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv1.conv.weight - torch.Size([672, 112, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv1.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv1.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv2.conv.weight - torch.Size([672, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv2.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv2.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv1.weight - torch.Size([28, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv1.bias - torch.Size([28]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv2.weight - torch.Size([672, 28, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv2.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv3.conv.weight - torch.Size([112, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv3.bn.weight - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv3.bn.bias - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv1.conv.weight - torch.Size([672, 112, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv1.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv1.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv2.conv.weight - torch.Size([672, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv2.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv2.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv1.weight - torch.Size([28, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv1.bias - torch.Size([28]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv2.weight - torch.Size([672, 28, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv2.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv3.conv.weight - torch.Size([192, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv3.bn.weight - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv3.bn.bias - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv1.conv.weight - torch.Size([1152, 192, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv1.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv1.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv2.conv.weight - torch.Size([1152, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv2.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv2.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv1.weight - torch.Size([48, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv1.bias - torch.Size([48]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv2.weight - torch.Size([1152, 48, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv2.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv3.conv.weight - torch.Size([192, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv3.bn.weight - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv3.bn.bias - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv1.conv.weight - torch.Size([1152, 192, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv1.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv1.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv2.conv.weight - torch.Size([1152, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv2.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv2.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.se.conv1.weight - torch.Size([48, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.se.conv1.bias - torch.Size([48]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.se.conv2.weight - torch.Size([1152, 48, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.se.conv2.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv3.conv.weight - torch.Size([192, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv3.bn.weight - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv3.bn.bias - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv1.conv.weight - torch.Size([1152, 192, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv1.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv1.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv2.conv.weight - torch.Size([1152, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv2.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv2.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.se.conv1.weight - torch.Size([48, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.se.conv1.bias - torch.Size([48]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.se.conv2.weight - torch.Size([1152, 48, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.se.conv2.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv3.conv.weight - torch.Size([192, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv3.bn.weight - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv3.bn.bias - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv1.conv.weight - torch.Size([1152, 192, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv1.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv1.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv2.conv.weight - torch.Size([1152, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv2.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv2.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.se.conv1.weight - torch.Size([48, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.se.conv1.bias - torch.Size([48]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.se.conv2.weight - torch.Size([1152, 48, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.se.conv2.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv3.conv.weight - torch.Size([320, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv3.bn.weight - torch.Size([320]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv3.bn.bias - torch.Size([320]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "backbone.features.final_block.conv.weight - torch.Size([1280, 320, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "backbone.features.final_block.bn.weight - torch.Size([1280]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "backbone.features.final_block.bn.bias - torch.Size([1280]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "head.fc.weight - torch.Size([102, 1280]): \n",
      "NormalInit: mean=0, std=0.01, bias=0 \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "head.fc.bias - torch.Size([102]): \n",
      "NormalInit: mean=0, std=0.01, bias=0 \n",
      " \n",
      "2023-07-10 18:25:57,371 | INFO : Task Adaptation: [] => ['BACKGROUND_Google', 'Faces', 'Faces_easy', 'Leopards', 'Motorbikes', 'accordion', 'airplanes', 'anchor', 'ant', 'barrel', 'bass', 'beaver', 'binocular', 'bonsai', 'brain', 'brontosaurus', 'buddha', 'butterfly', 'camera', 'cannon', 'car_side', 'ceiling_fan', 'cellphone', 'chair', 'chandelier', 'cougar_body', 'cougar_face', 'crab', 'crayfish', 'crocodile', 'crocodile_head', 'cup', 'dalmatian', 'dollar_bill', 'dolphin', 'dragonfly', 'electric_guitar', 'elephant', 'emu', 'euphonium', 'ewer', 'ferry', 'flamingo', 'flamingo_head', 'garfield', 'gerenuk', 'gramophone', 'grand_piano', 'hawksbill', 'headphone', 'hedgehog', 'helicopter', 'ibis', 'inline_skate', 'joshua_tree', 'kangaroo', 'ketch', 'lamp', 'laptop', 'llama', 'lobster', 'lotus', 'mandolin', 'mayfly', 'menorah', 'metronome', 'minaret', 'nautilus', 'octopus', 'okapi', 'pagoda', 'panda', 'pigeon', 'pizza', 'platypus', 'pyramid', 'revolver', 'rhino', 'rooster', 'saxophone', 'schooner', 'scissors', 'scorpion', 'sea_horse', 'snoopy', 'soccer_ball', 'stapler', 'starfish', 'stegosaurus', 'stop_sign', 'strawberry', 'sunflower', 'tick', 'trilobite', 'umbrella', 'watch', 'water_lilly', 'wheelchair', 'wild_cat', 'windsor_chair', 'wrench', 'yin_yang']\n",
      "2023-07-10 18:25:57,371 | INFO : - Efficient Mode: True\n",
      "2023-07-10 18:25:57,371 | INFO : - Sampler type: balanced\n",
      "2023-07-10 18:25:57,371 | INFO : - Sampler flag: False\n",
      "2023-07-10 18:25:57,372 - mmcls - INFO - Start running, host: dwekr@sooah-desktop, work_dir: /home/dwekr/workspace/datum/outputs/logs\n",
      "2023-07-10 18:25:57,372 - mmcls - INFO - Hooks will be executed in the following order:\n",
      "before_run:\n",
      "(VERY_HIGH   ) CosineAnnealingLrUpdaterHook       \n",
      "(ABOVE_NORMAL) Fp16SAMOptimizerHook               \n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) CheckpointHookWithValResults       \n",
      "(NORMAL      ) CancelInterfaceHook                \n",
      "(NORMAL      ) AdaptiveTrainSchedulingHook        \n",
      "(NORMAL      ) LoggerReplaceHook                  \n",
      "(71          ) OTXProgressHook                    \n",
      "(75          ) LazyEarlyStoppingHook              \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      " -------------------- \n",
      "before_train_epoch:\n",
      "(VERY_HIGH   ) CosineAnnealingLrUpdaterHook       \n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) TaskAdaptHook                      \n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      "(VERY_LOW    ) MemCacheHook                       \n",
      "(LOWEST      ) ForceTrainModeHook                 \n",
      " -------------------- \n",
      "before_train_iter:\n",
      "(VERY_HIGH   ) CosineAnnealingLrUpdaterHook       \n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) AdaptiveTrainSchedulingHook        \n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      " -------------------- \n",
      "after_train_iter:\n",
      "(ABOVE_NORMAL) Fp16SAMOptimizerHook               \n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) CheckpointHookWithValResults       \n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      "(75          ) LazyEarlyStoppingHook              \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      " -------------------- \n",
      "after_train_epoch:\n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) CheckpointHookWithValResults       \n",
      "(71          ) OTXProgressHook                    \n",
      "(75          ) LazyEarlyStoppingHook              \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      "(VERY_LOW    ) MemCacheHook                       \n",
      " -------------------- \n",
      "before_val_epoch:\n",
      "(NORMAL      ) TaskAdaptHook                      \n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      "(VERY_LOW    ) MemCacheHook                       \n",
      " -------------------- \n",
      "before_val_iter:\n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      " -------------------- \n",
      "after_val_iter:\n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      " -------------------- \n",
      "after_val_epoch:\n",
      "(71          ) OTXProgressHook                    \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      "(VERY_LOW    ) MemCacheHook                       \n",
      " -------------------- \n",
      "after_run:\n",
      "(NORMAL      ) CancelInterfaceHook                \n",
      "(71          ) OTXProgressHook                    \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      " -------------------- \n",
      "2023-07-10 18:25:57,372 - mmcls - INFO - workflow: [('train', 1)], max: 90 epochs\n",
      "2023-07-10 18:25:57,373 | INFO : cancel hook is initialized\n",
      "2023-07-10 18:25:57,373 | INFO : logger in the runner is replaced to the MPA logger\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/site-packages/torch/utils/tensorboard/__init__.py:4: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  if not hasattr(tensorboard, \"__version__\") or LooseVersion(\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/site-packages/torch/utils/tensorboard/__init__.py:6: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  ) < LooseVersion(\"1.15\"):\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1170.6 task/s, elapsed: 8s, ETA:     0s\n",
      "2023-07-10 18:26:18,923 | INFO : Epoch [1][100/143]\tlr: 4.900e-03, eta: 0:29:09, time: 0.137, data_time: 0.004, memory: 3668, current_iters: 99, loss: 1.8926, sharpness: 0.2329, max_loss: 2.1255\n",
      "2023-07-10 18:26:22,562 | WARNING : training progress 1%\n",
      "2023-07-10 18:26:24,335 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:26:24,335 | INFO : Epoch [1][143/143]\tlr: 4.900e-03, eta: 0:39:08, time: 0.127, data_time: 0.000, memory: 3668, current_iters: 142, loss: 0.8262, sharpness: 0.2382, max_loss: 1.0639\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1378.6 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:26:31,004 | INFO : Saving best checkpoint at 1 epochs\n",
      "2023-07-10 18:26:31,133 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:26:31,133 | INFO : Epoch(val) [1][143]\taccuracy_top-1: 0.9555, accuracy_top-5: 0.9953, BACKGROUND_Google accuracy: 0.8651, Faces accuracy: 0.9954, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 0.8571, ant accuracy: 0.7381, barrel accuracy: 0.9574, bass accuracy: 0.8889, beaver accuracy: 0.9348, binocular accuracy: 0.9697, bonsai accuracy: 0.9922, brain accuracy: 0.9898, brontosaurus accuracy: 0.7674, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 0.9800, cannon accuracy: 0.9302, car_side accuracy: 1.0000, ceiling_fan accuracy: 0.9149, cellphone accuracy: 1.0000, chair accuracy: 0.8710, chandelier accuracy: 0.9720, cougar_body accuracy: 0.9574, cougar_face accuracy: 0.9420, crab accuracy: 0.9863, crayfish accuracy: 0.9000, crocodile accuracy: 0.9400, crocodile_head accuracy: 0.4510, cup accuracy: 1.0000, dalmatian accuracy: 0.9851, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 0.9200, elephant accuracy: 0.9219, emu accuracy: 0.9623, euphonium accuracy: 0.9844, ewer accuracy: 0.9882, ferry accuracy: 1.0000, flamingo accuracy: 0.9851, flamingo_head accuracy: 0.9778, garfield accuracy: 0.9118, gerenuk accuracy: 0.7941, gramophone accuracy: 1.0000, grand_piano accuracy: 0.9899, hawksbill accuracy: 1.0000, headphone accuracy: 0.9524, hedgehog accuracy: 0.9815, helicopter accuracy: 0.9886, ibis accuracy: 0.9875, inline_skate accuracy: 0.8710, joshua_tree accuracy: 0.9844, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 0.9615, lobster accuracy: 0.5854, lotus accuracy: 0.8939, mandolin accuracy: 0.8140, mayfly accuracy: 0.8750, menorah accuracy: 0.9885, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 0.9455, octopus accuracy: 0.2857, okapi accuracy: 0.9744, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 0.9778, pizza accuracy: 0.9623, platypus accuracy: 0.8529, pyramid accuracy: 1.0000, revolver accuracy: 0.9756, rhino accuracy: 0.9661, rooster accuracy: 0.9388, saxophone accuracy: 1.0000, schooner accuracy: 0.2381, scissors accuracy: 1.0000, scorpion accuracy: 0.9286, sea_horse accuracy: 0.9474, snoopy accuracy: 0.6571, soccer_ball accuracy: 1.0000, stapler accuracy: 0.9333, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 0.9429, sunflower accuracy: 0.9882, tick accuracy: 0.9796, trilobite accuracy: 0.9884, umbrella accuracy: 0.9867, watch accuracy: 0.9958, water_lilly accuracy: 0.5946, wheelchair accuracy: 0.9661, wild_cat accuracy: 0.8235, windsor_chair accuracy: 1.0000, wrench accuracy: 0.8974, yin_yang accuracy: 0.9500, mean accuracy: 0.9293, accuracy: 0.9555, current_iters: 143\n",
      "2023-07-10 18:26:31,139 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:26:44,023 | INFO : Epoch [2][100/143]\tlr: 4.899e-03, eta: 0:34:00, time: 0.129, data_time: 0.003, memory: 3668, current_iters: 242, loss: 0.2356, sharpness: 0.1889, max_loss: 0.4245\n",
      "2023-07-10 18:26:49,454 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:26:49,454 | INFO : Epoch [2][143/143]\tlr: 4.899e-03, eta: 0:38:04, time: 0.126, data_time: 0.000, memory: 3668, current_iters: 285, loss: 0.2175, sharpness: 0.1879, max_loss: 0.4054\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1342.2 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:26:56,302 | INFO : Saving best checkpoint at 2 epochs\n",
      "2023-07-10 18:26:56,454 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:26:56,454 | INFO : Epoch(val) [2][143]\taccuracy_top-1: 0.9843, accuracy_top-5: 0.9996, BACKGROUND_Google accuracy: 0.9422, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 0.9762, ant accuracy: 0.9524, barrel accuracy: 1.0000, bass accuracy: 0.9815, beaver accuracy: 0.9783, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 0.9070, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 0.9535, car_side accuracy: 1.0000, ceiling_fan accuracy: 0.9787, cellphone accuracy: 1.0000, chair accuracy: 0.9839, chandelier accuracy: 0.9813, cougar_body accuracy: 0.9787, cougar_face accuracy: 0.9710, crab accuracy: 0.9589, crayfish accuracy: 0.9714, crocodile accuracy: 0.9400, crocodile_head accuracy: 0.9412, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 0.9333, garfield accuracy: 0.8824, gerenuk accuracy: 0.9706, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 0.9524, hedgehog accuracy: 0.9815, helicopter accuracy: 0.9886, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 0.9688, kangaroo accuracy: 1.0000, ketch accuracy: 0.8947, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.7805, lotus accuracy: 0.9242, mandolin accuracy: 0.9070, mayfly accuracy: 0.9750, menorah accuracy: 0.9885, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 0.8857, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 0.9778, pizza accuracy: 1.0000, platypus accuracy: 0.8824, pyramid accuracy: 0.9825, revolver accuracy: 0.9878, rhino accuracy: 0.9831, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 0.9524, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 0.9825, snoopy accuracy: 0.9714, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 0.9867, watch accuracy: 1.0000, water_lilly accuracy: 0.5135, wheelchair accuracy: 1.0000, wild_cat accuracy: 0.9706, windsor_chair accuracy: 0.9821, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9764, accuracy: 0.9843, current_iters: 286\n",
      "2023-07-10 18:26:56,459 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:27:09,381 | INFO : Epoch [3][100/143]\tlr: 4.894e-03, eta: 0:34:56, time: 0.129, data_time: 0.003, memory: 3668, current_iters: 385, loss: 0.1279, sharpness: 0.1522, max_loss: 0.2801\n",
      "2023-07-10 18:27:14,812 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:27:14,813 | INFO : Epoch [3][143/143]\tlr: 4.894e-03, eta: 0:37:26, time: 0.126, data_time: 0.000, memory: 3668, current_iters: 428, loss: 0.1352, sharpness: 0.1578, max_loss: 0.2931\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1350.0 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:27:21,623 | INFO : Saving best checkpoint at 3 epochs\n",
      "2023-07-10 18:27:21,769 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:27:21,769 | INFO : Epoch(val) [3][143]\taccuracy_top-1: 0.9892, accuracy_top-5: 0.9998, BACKGROUND_Google accuracy: 0.9893, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 0.9762, barrel accuracy: 1.0000, bass accuracy: 0.9630, beaver accuracy: 1.0000, binocular accuracy: 0.9697, bonsai accuracy: 0.9922, brain accuracy: 1.0000, brontosaurus accuracy: 0.9535, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 0.9787, cellphone accuracy: 1.0000, chair accuracy: 0.9194, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 0.9710, crab accuracy: 0.9863, crayfish accuracy: 1.0000, crocodile accuracy: 0.9000, crocodile_head accuracy: 0.9608, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 0.8667, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 0.9706, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.8772, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 0.9872, lobster accuracy: 0.7561, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 0.9885, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 0.9412, pyramid accuracy: 1.0000, revolver accuracy: 0.9878, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 0.9841, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 0.9825, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 0.9831, stop_sign accuracy: 1.0000, strawberry accuracy: 0.9714, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.4324, wheelchair accuracy: 0.9831, wild_cat accuracy: 0.9706, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9827, accuracy: 0.9892, current_iters: 429\n",
      "2023-07-10 18:27:21,774 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:27:34,766 | INFO : Epoch [4][100/143]\tlr: 4.887e-03, eta: 0:35:10, time: 0.130, data_time: 0.003, memory: 3668, current_iters: 528, loss: 0.0870, sharpness: 0.1324, max_loss: 0.2194\n",
      "2023-07-10 18:27:40,221 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:27:40,221 | INFO : Epoch [4][143/143]\tlr: 4.887e-03, eta: 0:36:58, time: 0.127, data_time: 0.000, memory: 3668, current_iters: 571, loss: 0.0855, sharpness: 0.1296, max_loss: 0.2151\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1329.8 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:27:47,138 | INFO : Saving best checkpoint at 4 epochs\n",
      "2023-07-10 18:27:47,296 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:27:47,297 | INFO : Epoch(val) [4][143]\taccuracy_top-1: 0.9929, accuracy_top-5: 0.9999, BACKGROUND_Google accuracy: 0.9893, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 0.9762, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 0.9767, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 0.9787, cellphone accuracy: 1.0000, chair accuracy: 0.9839, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 0.9710, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.7600, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9211, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.7561, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 0.9750, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 0.9714, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 0.9878, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.5946, wheelchair accuracy: 1.0000, wild_cat accuracy: 0.9706, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9883, accuracy: 0.9929, current_iters: 572\n",
      "2023-07-10 18:27:47,303 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:28:00,299 | INFO : Epoch [5][100/143]\tlr: 4.876e-03, eta: 0:35:08, time: 0.130, data_time: 0.002, memory: 3668, current_iters: 671, loss: 0.0630, sharpness: 0.1146, max_loss: 0.1776\n",
      "2023-07-10 18:28:05,805 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:28:05,806 | INFO : Epoch [5][143/143]\tlr: 4.876e-03, eta: 0:36:32, time: 0.128, data_time: 0.000, memory: 3668, current_iters: 714, loss: 0.0684, sharpness: 0.1209, max_loss: 0.1893\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1331.3 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:28:12,712 | INFO : Saving best checkpoint at 5 epochs\n",
      "2023-07-10 18:28:12,872 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:28:12,872 | INFO : Epoch(val) [5][143]\taccuracy_top-1: 0.9942, accuracy_top-5: 0.9998, BACKGROUND_Google accuracy: 0.9936, Faces accuracy: 0.9977, Faces_easy accuracy: 0.9931, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 0.9839, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 0.9855, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.9800, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.8860, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.7805, lotus accuracy: 0.7879, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 0.9841, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 0.8857, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.9459, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9922, accuracy: 0.9942, current_iters: 715\n",
      "2023-07-10 18:28:12,878 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:28:25,985 | INFO : Epoch [6][100/143]\tlr: 4.863e-03, eta: 0:35:01, time: 0.131, data_time: 0.003, memory: 3668, current_iters: 814, loss: 0.0465, sharpness: 0.1021, max_loss: 0.1486\n",
      "2023-07-10 18:28:31,501 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:28:31,501 | INFO : Epoch [6][143/143]\tlr: 4.863e-03, eta: 0:36:08, time: 0.128, data_time: 0.000, memory: 3668, current_iters: 857, loss: 0.0486, sharpness: 0.1058, max_loss: 0.1545\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1336.2 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:28:38,381 | INFO : Saving best checkpoint at 6 epochs\n",
      "2023-07-10 18:28:38,533 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:28:38,533 | INFO : Epoch(val) [6][143]\taccuracy_top-1: 0.9960, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 0.9979, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 0.9855, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.9600, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 0.9851, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9912, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.8293, lotus accuracy: 0.8485, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 0.9714, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 0.8730, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.9730, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9942, accuracy: 0.9960, current_iters: 858\n",
      "2023-07-10 18:28:38,539 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:28:51,778 | INFO : Epoch [7][100/143]\tlr: 4.846e-03, eta: 0:34:50, time: 0.132, data_time: 0.003, memory: 3668, current_iters: 957, loss: 0.0439, sharpness: 0.0961, max_loss: 0.1400\n",
      "2023-07-10 18:28:57,353 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:28:57,354 | INFO : Epoch [7][143/143]\tlr: 4.846e-03, eta: 0:35:47, time: 0.130, data_time: 0.000, memory: 3668, current_iters: 1000, loss: 0.0388, sharpness: 0.0918, max_loss: 0.1306\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1314.9 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:29:04,348 | INFO : Saving best checkpoint at 7 epochs\n",
      "2023-07-10 18:29:04,504 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:29:04,504 | INFO : Epoch(val) [7][143]\taccuracy_top-1: 0.9968, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 0.9979, Faces accuracy: 0.9977, Faces_easy accuracy: 0.9931, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 0.9839, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 0.9855, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.9600, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9561, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.9756, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.6216, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9948, accuracy: 0.9968, current_iters: 1001\n",
      "2023-07-10 18:29:04,510 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:29:17,693 | INFO : Epoch [8][100/143]\tlr: 4.827e-03, eta: 0:34:36, time: 0.132, data_time: 0.003, memory: 3668, current_iters: 1100, loss: 0.0333, sharpness: 0.0873, max_loss: 0.1206\n",
      "2023-07-10 18:29:23,292 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:29:23,292 | INFO : Epoch [8][143/143]\tlr: 4.827e-03, eta: 0:35:24, time: 0.130, data_time: 0.000, memory: 3668, current_iters: 1143, loss: 0.0333, sharpness: 0.0879, max_loss: 0.1212\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1311.3 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:29:30,300 | INFO : Saving best checkpoint at 8 epochs\n",
      "2023-07-10 18:29:30,449 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:29:30,449 | INFO : Epoch(val) [8][143]\taccuracy_top-1: 0.9981, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 0.9855, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.9800, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9737, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.9756, lotus accuracy: 0.9545, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 0.9434, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.9730, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9978, accuracy: 0.9981, current_iters: 1144\n",
      "2023-07-10 18:29:30,455 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:29:43,771 | INFO : Epoch [9][100/143]\tlr: 4.805e-03, eta: 0:34:21, time: 0.133, data_time: 0.003, memory: 3668, current_iters: 1243, loss: 0.0331, sharpness: 0.0866, max_loss: 0.1196\n",
      "2023-07-10 18:29:49,375 | WARNING : training progress 10%\n",
      "2023-07-10 18:29:49,376 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:29:49,376 | INFO : Epoch [9][143/143]\tlr: 4.805e-03, eta: 0:35:02, time: 0.131, data_time: 0.000, memory: 3668, current_iters: 1286, loss: 0.0321, sharpness: 0.0835, max_loss: 0.1156\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1320.3 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:29:56,340 | INFO : Saving best checkpoint at 9 epochs\n",
      "2023-07-10 18:29:56,490 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:29:56,490 | INFO : Epoch(val) [9][143]\taccuracy_top-1: 0.9986, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.9800, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9825, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.9756, lotus accuracy: 0.9394, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 0.9825, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9985, accuracy: 0.9986, current_iters: 1287\n",
      "2023-07-10 18:29:56,496 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:30:09,767 | INFO : Epoch [10][100/143]\tlr: 4.780e-03, eta: 0:34:03, time: 0.133, data_time: 0.003, memory: 3668, current_iters: 1386, loss: 0.0246, sharpness: 0.0816, max_loss: 0.1062\n",
      "2023-07-10 18:30:15,371 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:30:15,371 | INFO : Epoch [10][143/143]\tlr: 4.780e-03, eta: 0:34:39, time: 0.130, data_time: 0.000, memory: 3668, current_iters: 1429, loss: 0.0256, sharpness: 0.0801, max_loss: 0.1056\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1318.8 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:30:22,344 | INFO : Saving best checkpoint at 10 epochs\n",
      "2023-07-10 18:30:22,482 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:30:22,482 | INFO : Epoch(val) [10][143]\taccuracy_top-1: 0.9988, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 0.9800, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.9800, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 0.9884, ketch accuracy: 0.9912, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.9756, lotus accuracy: 0.9697, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9988, accuracy: 0.9988, current_iters: 1430\n",
      "2023-07-10 18:30:22,488 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:30:35,822 | INFO : Epoch [11][100/143]\tlr: 4.752e-03, eta: 0:33:45, time: 0.133, data_time: 0.003, memory: 3668, current_iters: 1529, loss: 0.0211, sharpness: 0.0742, max_loss: 0.0953\n",
      "2023-07-10 18:30:41,414 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:30:41,414 | INFO : Epoch [11][143/143]\tlr: 4.752e-03, eta: 0:34:16, time: 0.131, data_time: 0.000, memory: 3668, current_iters: 1572, loss: 0.0230, sharpness: 0.0762, max_loss: 0.0992\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1296.6 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:30:48,503 | INFO : Saving best checkpoint at 11 epochs\n",
      "2023-07-10 18:30:48,654 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:30:48,654 | INFO : Epoch(val) [11][143]\taccuracy_top-1: 0.9991, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9825, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 0.9697, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9994, accuracy: 0.9991, current_iters: 1573\n",
      "2023-07-10 18:30:48,660 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:31:01,987 | INFO : Epoch [12][100/143]\tlr: 4.722e-03, eta: 0:33:25, time: 0.133, data_time: 0.003, memory: 3668, current_iters: 1672, loss: 0.0193, sharpness: 0.0685, max_loss: 0.0878\n",
      "2023-07-10 18:31:07,641 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:31:07,641 | INFO : Epoch [12][143/143]\tlr: 4.722e-03, eta: 0:33:53, time: 0.131, data_time: 0.000, memory: 3668, current_iters: 1715, loss: 0.0197, sharpness: 0.0717, max_loss: 0.0915\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1311.1 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:31:14,723 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:31:14,723 | INFO : Epoch(val) [12][143]\taccuracy_top-1: 0.9988, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9561, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 0.9697, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9992, accuracy: 0.9988, current_iters: 1716\n",
      "2023-07-10 18:31:14,729 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:31:28,106 | INFO : Epoch [13][100/143]\tlr: 4.688e-03, eta: 0:33:05, time: 0.134, data_time: 0.003, memory: 3668, current_iters: 1815, loss: 0.0182, sharpness: 0.0729, max_loss: 0.0911\n",
      "2023-07-10 18:31:33,745 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:31:33,746 | INFO : Epoch [13][143/143]\tlr: 4.688e-03, eta: 0:33:29, time: 0.131, data_time: 0.000, memory: 3668, current_iters: 1858, loss: 0.0175, sharpness: 0.0714, max_loss: 0.0890\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1291.1 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:31:40,865 | INFO : Saving best checkpoint at 13 epochs\n",
      "2023-07-10 18:31:41,009 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:31:41,009 | INFO : Epoch(val) [13][143]\taccuracy_top-1: 0.9992, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.9800, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 0.9697, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9994, accuracy: 0.9992, current_iters: 1859\n",
      "2023-07-10 18:31:41,014 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:31:54,354 | INFO : Epoch [14][100/143]\tlr: 4.652e-03, eta: 0:32:44, time: 0.133, data_time: 0.003, memory: 3668, current_iters: 1958, loss: 0.0161, sharpness: 0.0669, max_loss: 0.0830\n",
      "2023-07-10 18:31:59,981 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:31:59,981 | INFO : Epoch [14][143/143]\tlr: 4.652e-03, eta: 0:33:05, time: 0.131, data_time: 0.000, memory: 3668, current_iters: 2001, loss: 0.0157, sharpness: 0.0655, max_loss: 0.0813\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1287.1 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:32:07,122 | INFO : Saving best checkpoint at 14 epochs\n",
      "2023-07-10 18:32:07,258 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:32:07,258 | INFO : Epoch(val) [14][143]\taccuracy_top-1: 0.9996, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9999, accuracy: 0.9996, current_iters: 2002\n",
      "2023-07-10 18:32:07,264 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:32:20,583 | INFO : Epoch [15][100/143]\tlr: 4.613e-03, eta: 0:32:21, time: 0.133, data_time: 0.003, memory: 3668, current_iters: 2101, loss: 0.0148, sharpness: 0.0600, max_loss: 0.0748\n",
      "2023-07-10 18:32:26,199 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:32:26,200 | INFO : Epoch [15][143/143]\tlr: 4.613e-03, eta: 0:32:40, time: 0.131, data_time: 0.000, memory: 3668, current_iters: 2144, loss: 0.0143, sharpness: 0.0609, max_loss: 0.0753\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1302.0 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:32:33,334 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:32:33,334 | INFO : Epoch(val) [15][143]\taccuracy_top-1: 0.9992, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 0.9697, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 0.9841, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9995, accuracy: 0.9992, current_iters: 2145\n",
      "2023-07-10 18:32:33,340 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:32:46,782 | INFO : Epoch [16][100/143]\tlr: 4.572e-03, eta: 0:31:59, time: 0.134, data_time: 0.003, memory: 3668, current_iters: 2244, loss: 0.0135, sharpness: 0.0594, max_loss: 0.0730\n",
      "2023-07-10 18:32:52,416 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:32:52,416 | INFO : Epoch [16][143/143]\tlr: 4.572e-03, eta: 0:32:16, time: 0.132, data_time: 0.000, memory: 3668, current_iters: 2287, loss: 0.0139, sharpness: 0.0622, max_loss: 0.0761\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1285.5 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:32:59,656 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:32:59,656 | INFO : Epoch(val) [16][143]\taccuracy_top-1: 0.9992, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 0.9977, Faces_easy accuracy: 0.9931, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 0.9697, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 0.9821, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9994, accuracy: 0.9992, current_iters: 2288\n",
      "2023-07-10 18:32:59,663 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:33:13,068 | INFO : Epoch [17][100/143]\tlr: 4.528e-03, eta: 0:31:36, time: 0.134, data_time: 0.003, memory: 3668, current_iters: 2387, loss: 0.0123, sharpness: 0.0587, max_loss: 0.0710\n",
      "2023-07-10 18:33:18,743 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:33:18,743 | INFO : Epoch [17][143/143]\tlr: 4.528e-03, eta: 0:31:52, time: 0.132, data_time: 0.000, memory: 3668, current_iters: 2430, loss: 0.0127, sharpness: 0.0581, max_loss: 0.0708\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1304.0 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:33:25,793 | INFO : Saving best checkpoint at 17 epochs\n",
      "2023-07-10 18:33:25,945 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:33:25,945 | INFO : Epoch(val) [17][143]\taccuracy_top-1: 0.9996, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 0.9931, Faces_easy accuracy: 0.9977, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9999, accuracy: 0.9996, current_iters: 2431\n",
      "2023-07-10 18:33:25,951 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:33:39,372 | INFO : Epoch [18][100/143]\tlr: 4.481e-03, eta: 0:31:13, time: 0.134, data_time: 0.003, memory: 3668, current_iters: 2530, loss: 0.0110, sharpness: 0.0569, max_loss: 0.0680\n",
      "2023-07-10 18:33:45,053 | WARNING : training progress 20%\n",
      "2023-07-10 18:33:45,054 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:33:45,054 | INFO : Epoch [18][143/143]\tlr: 4.481e-03, eta: 0:31:27, time: 0.132, data_time: 0.000, memory: 3668, current_iters: 2573, loss: 0.0121, sharpness: 0.0599, max_loss: 0.0719\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1295.6 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:33:52,227 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:33:52,227 | INFO : Epoch(val) [18][143]\taccuracy_top-1: 0.9995, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 0.9855, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9998, accuracy: 0.9995, current_iters: 2574\n",
      "2023-07-10 18:33:52,233 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:34:05,654 | INFO : Epoch [19][100/143]\tlr: 4.432e-03, eta: 0:30:50, time: 0.134, data_time: 0.003, memory: 3668, current_iters: 2673, loss: 0.0125, sharpness: 0.0554, max_loss: 0.0679\n",
      "2023-07-10 18:34:11,362 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:34:11,363 | INFO : Epoch [19][143/143]\tlr: 4.432e-03, eta: 0:31:02, time: 0.132, data_time: 0.000, memory: 3668, current_iters: 2716, loss: 0.0126, sharpness: 0.0569, max_loss: 0.0696\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1296.8 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:34:18,528 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:34:18,528 | INFO : Epoch(val) [19][143]\taccuracy_top-1: 0.9993, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 0.9412, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9993, accuracy: 0.9993, current_iters: 2717\n",
      "2023-07-10 18:34:18,534 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:34:31,987 | INFO : Epoch [20][100/143]\tlr: 4.381e-03, eta: 0:30:26, time: 0.135, data_time: 0.003, memory: 3668, current_iters: 2816, loss: 0.0096, sharpness: 0.0592, max_loss: 0.0688\n",
      "2023-07-10 18:34:37,766 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:34:37,766 | INFO : Epoch [20][143/143]\tlr: 4.381e-03, eta: 0:30:38, time: 0.133, data_time: 0.000, memory: 3668, current_iters: 2859, loss: 0.0099, sharpness: 0.0539, max_loss: 0.0639\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1295.7 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:34:44,863 | INFO : Saving best checkpoint at 20 epochs\n",
      "2023-07-10 18:34:45,014 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:34:45,014 | INFO : Epoch(val) [20][143]\taccuracy_top-1: 0.9996, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9999, accuracy: 0.9996, current_iters: 2860\n",
      "2023-07-10 18:34:45,020 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:34:58,512 | INFO : Epoch [21][100/143]\tlr: 4.327e-03, eta: 0:30:03, time: 0.135, data_time: 0.003, memory: 3668, current_iters: 2959, loss: 0.0098, sharpness: 0.0531, max_loss: 0.0629\n",
      "2023-07-10 18:35:04,235 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:35:04,235 | INFO : Epoch [21][143/143]\tlr: 4.327e-03, eta: 0:30:13, time: 0.133, data_time: 0.000, memory: 3668, current_iters: 3002, loss: 0.0119, sharpness: 0.0539, max_loss: 0.0657\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1285.6 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:35:11,387 | INFO : Saving best checkpoint at 21 epochs\n",
      "2023-07-10 18:35:11,604 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:35:11,604 | INFO : Epoch(val) [21][143]\taccuracy_top-1: 0.9996, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9999, accuracy: 0.9996, current_iters: 3003\n",
      "2023-07-10 18:35:11,609 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:35:25,076 | INFO : Epoch [22][100/143]\tlr: 4.271e-03, eta: 0:29:39, time: 0.135, data_time: 0.003, memory: 3668, current_iters: 3102, loss: 0.0088, sharpness: 0.0485, max_loss: 0.0572\n",
      "2023-07-10 18:35:30,824 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:35:30,824 | INFO : Epoch [22][143/143]\tlr: 4.271e-03, eta: 0:29:48, time: 0.133, data_time: 0.000, memory: 3668, current_iters: 3145, loss: 0.0083, sharpness: 0.0470, max_loss: 0.0553\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1277.8 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:35:38,162 | INFO : \n",
      "Early Stopping at :21 with best accuracy: 0.9995625305175782\n",
      "2023-07-10 18:35:38,163 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:35:38,163 | INFO : Epoch(val) [22][143]\taccuracy_top-1: 0.9993, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 0.9697, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9996, accuracy: 0.9993, current_iters: 3146\n",
      "2023-07-10 18:35:38,176 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:35:39,248 | INFO : called save_model\n",
      "2023-07-10 18:35:39,356 | INFO : Final model performance: Performance(score: 0.9995625305175782, dashboard: (115 metric groups))\n",
      "2023-07-10 18:35:39,357 | INFO : train done.\n",
      "otx train time elapsed:  0:09:51.762842\n",
      "otx train CLI report has been generated: outputs/cli_report.log\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/tempfile.py:833: ResourceWarning: Implicitly cleaning up <TemporaryDirectory '/tmp/img-cache-iqz52wv7'>\n",
      "  _warnings.warn(warn_message, ResourceWarning)\n"
     ]
    }
   ],
   "source": [
    "!otx train EfficientNet-B0 \\\n",
    "    --train-data-roots caltech-101 \\\n",
    "    --val-data-roots caltech-101 \\\n",
    "    -o outputs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9957f4b9",
   "metadata": {},
   "source": [
    "When training with the entire caltech-101 dataset, the best accuracy is `0.9995625305175782` and the training time is 9 minutes and 51 seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2a677df4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*] Workspace Path: otx-workspace-CLASSIFICATION\n",
      "[*] Load Model Template ID: Custom_Image_Classification_EfficinetNet-B0\n",
      "[*] Load Model Name: EfficientNet-B0\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details.\n",
      "  warnings.warn(\n",
      "2023-07-08 03:58:52,794 | WARNING : Duplicate key is detected among bases [{'model'}]\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/model.py\n",
      "2023-07-08 03:58:52,823 | WARNING : Duplicate key is detected among bases [{'model'}]\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/model_multilabel.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/data_pipeline.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/deployment.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/hpo_config.yaml\n",
      "2023-07-08 03:58:52,923 | WARNING : Duplicate key is detected among bases [{'model'}]\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/model_hierarchical.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/compression_config.json\n",
      "[*] Update data configuration file to: otx-workspace-CLASSIFICATION/data.yaml\n",
      "2023-07-08 03:58:53.770452: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/site-packages/openvino/pyopenvino/__init__.py:10: FutureWarning: The module is private and following namespace `pyopenvino` will be removed in the future\n",
      "  warnings.warn(message=\"The module is private and following namespace \" \"`pyopenvino` will be removed in the future\", category=FutureWarning)\n",
      "2023-07-08 03:58:55,877 | INFO : Classification mode: multiclass\n",
      "2023-07-08 03:58:55,877 | INFO : train()\n",
      "2023-07-08 03:58:55,887 | INFO : Training seed was set to 5 w/ deterministic=False.\n",
      "2023-07-08 03:58:55,889 | INFO : Try to create a 0 size memory pool.\n",
      "2023-07-08 03:58:56,669 | INFO : configure!: training=True\n",
      "2023-07-08 03:58:56,783 | INFO : init weight - https://github.com/osmr/imgclsmob/releases/download/v0.0.364/efficientnet_b0-0752-0e386130.pth.zip\n",
      "2023-07-08 03:58:56,807 | INFO : 'in_channels' config in model.head is updated from -1 to 1280\n",
      "2023-07-08 03:58:56,808 | INFO : configure_data()\n",
      "2023-07-08 03:58:56,808 | INFO : task config!!!!: training=True\n",
      "2023-07-08 03:58:56,808 | INFO : train!\n",
      "2023-07-08 03:58:56,808 | INFO : cfg.gpu_ids = range(0, 1), distributed = False\n",
      "2023-07-08 03:58:56,826 | INFO : Environment info:\n",
      "------------------------------------------------------------\n",
      "sys.platform: linux\n",
      "Python: 3.10.0 (default, Mar  3 2022, 09:58:08) [GCC 7.5.0]\n",
      "CUDA available: True\n",
      "GPU 0,1: GeForce RTX 3090\n",
      "CUDA_HOME: /usr/local/cuda\n",
      "NVCC: Cuda compilation tools, release 11.1, V11.1.74\n",
      "GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
      "PyTorch: 1.13.1+cu117\n",
      "PyTorch compiling details: PyTorch built with:\n",
      "  - GCC 9.3\n",
      "  - C++ Version: 201402\n",
      "  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n",
      "  - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\n",
      "  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n",
      "  - LAPACK is enabled (usually provided by MKL)\n",
      "  - NNPACK is enabled\n",
      "  - CPU capability usage: AVX2\n",
      "  - CUDA Runtime 11.7\n",
      "  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n",
      "  - CuDNN 8.5\n",
      "  - Magma 2.6.1\n",
      "  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n",
      "\n",
      "TorchVision: 0.14.1+cu117\n",
      "OpenCV: 4.7.0\n",
      "MMCV: 1.7.1\n",
      "MMCV Compiler: GCC 7.5\n",
      "MMCV CUDA Compiler: 11.1\n",
      "MMClassification: 0.25.0+c5ac764\n",
      "------------------------------------------------------------\n",
      "\n",
      "2023-07-08 03:58:57,061 | INFO : init weight - https://github.com/osmr/imgclsmob/releases/download/v0.0.364/efficientnet_b0-0752-0e386130.pth.zip\n",
      "2023-07-08 03:58:57,072 - mmcv - INFO - initialize CustomLinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01}\n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.init_block.conv.conv.weight - torch.Size([32, 3, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.init_block.conv.bn.weight - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.init_block.conv.bn.bias - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.dw_conv.conv.weight - torch.Size([32, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.dw_conv.bn.weight - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.dw_conv.bn.bias - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv1.weight - torch.Size([8, 32, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv1.bias - torch.Size([8]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv2.weight - torch.Size([32, 8, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv2.bias - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.pw_conv.conv.weight - torch.Size([16, 32, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.pw_conv.bn.weight - torch.Size([16]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.pw_conv.bn.bias - torch.Size([16]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv1.conv.weight - torch.Size([96, 16, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv1.bn.weight - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv1.bn.bias - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv2.conv.weight - torch.Size([96, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv2.bn.weight - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv2.bn.bias - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv1.weight - torch.Size([4, 96, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv1.bias - torch.Size([4]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv2.weight - torch.Size([96, 4, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv2.bias - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv3.conv.weight - torch.Size([24, 96, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv3.bn.weight - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv3.bn.bias - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv1.conv.weight - torch.Size([144, 24, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv1.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv1.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv2.conv.weight - torch.Size([144, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv2.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv2.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv1.weight - torch.Size([6, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv1.bias - torch.Size([6]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv2.weight - torch.Size([144, 6, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv2.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv3.conv.weight - torch.Size([24, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv3.bn.weight - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv3.bn.bias - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv1.conv.weight - torch.Size([144, 24, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv1.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv1.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv2.conv.weight - torch.Size([144, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv2.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv2.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv1.weight - torch.Size([6, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv1.bias - torch.Size([6]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv2.weight - torch.Size([144, 6, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv2.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv3.conv.weight - torch.Size([40, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv3.bn.weight - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv3.bn.bias - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv1.conv.weight - torch.Size([240, 40, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv1.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv1.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv2.conv.weight - torch.Size([240, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv2.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv2.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv1.weight - torch.Size([10, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv1.bias - torch.Size([10]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv2.weight - torch.Size([240, 10, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv2.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv3.conv.weight - torch.Size([40, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv3.bn.weight - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv3.bn.bias - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv1.conv.weight - torch.Size([240, 40, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv1.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv1.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv2.conv.weight - torch.Size([240, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv2.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv2.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv1.weight - torch.Size([10, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv1.bias - torch.Size([10]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv2.weight - torch.Size([240, 10, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv2.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv3.conv.weight - torch.Size([80, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv3.bn.weight - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv3.bn.bias - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv1.conv.weight - torch.Size([480, 80, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv1.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv1.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv2.conv.weight - torch.Size([480, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv2.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv2.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv1.weight - torch.Size([20, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv1.bias - torch.Size([20]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv2.weight - torch.Size([480, 20, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv2.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv3.conv.weight - torch.Size([80, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv3.bn.weight - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv3.bn.bias - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv1.conv.weight - torch.Size([480, 80, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv1.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv1.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv2.conv.weight - torch.Size([480, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv2.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv2.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv1.weight - torch.Size([20, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv1.bias - torch.Size([20]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv2.weight - torch.Size([480, 20, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv2.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv3.conv.weight - torch.Size([80, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv3.bn.weight - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv3.bn.bias - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv1.conv.weight - torch.Size([480, 80, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv1.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv1.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv2.conv.weight - torch.Size([480, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv2.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv2.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv1.weight - torch.Size([20, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv1.bias - torch.Size([20]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv2.weight - torch.Size([480, 20, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv2.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv3.conv.weight - torch.Size([112, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv3.bn.weight - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv3.bn.bias - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv1.conv.weight - torch.Size([672, 112, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv1.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv1.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv2.conv.weight - torch.Size([672, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv2.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv2.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv1.weight - torch.Size([28, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv1.bias - torch.Size([28]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv2.weight - torch.Size([672, 28, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv2.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv3.conv.weight - torch.Size([112, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv3.bn.weight - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv3.bn.bias - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv1.conv.weight - torch.Size([672, 112, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv1.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv1.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv2.conv.weight - torch.Size([672, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv2.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv2.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv1.weight - torch.Size([28, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv1.bias - torch.Size([28]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv2.weight - torch.Size([672, 28, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv2.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv3.conv.weight - torch.Size([112, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv3.bn.weight - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv3.bn.bias - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv1.conv.weight - torch.Size([672, 112, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv1.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv1.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv2.conv.weight - torch.Size([672, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv2.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv2.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv1.weight - torch.Size([28, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv1.bias - torch.Size([28]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv2.weight - torch.Size([672, 28, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv2.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv3.conv.weight - torch.Size([192, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv3.bn.weight - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv3.bn.bias - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv1.conv.weight - torch.Size([1152, 192, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv1.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv1.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv2.conv.weight - torch.Size([1152, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv2.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv2.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv1.weight - torch.Size([48, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv1.bias - torch.Size([48]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv2.weight - torch.Size([1152, 48, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv2.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "ba