Skip to main content
Ctrl+K
Logo image Logo image
  • GitHub
  • GitHub

Section Navigation

Get Started

  • Introduction
  • Installation
  • OpenVINO™ Training Extensions CLI commands

Tutorials

  • Base Tutorials
    • How to train, validate, export and optimize the model
      • Classification model
      • Object Detection model
      • Instance Segmentation model
      • Semantic Segmentation model
      • Anomaly Detection Tutorial
      • Action Classification model
      • Action Detection model
    • How to run the demonstration mode with OpenVINO™ Training Extensions CLI
    • How to deploy the model and use demo in exportable code
    • How to explain the model behavior
  • Advanced Tutorials
    • Use Semi-Supervised Learning
    • Use Self-Supervised Learning
    • Backbone Replacement
    • Utilize OpenVINO™ Training Extensions APIs in your project
    • Simple HPO Tutorial

Explanation

  • Algorithms
    • Classification
      • Multi-class Classification
      • Multi-label Classification
      • Hierarchical Classification
    • Object Detection
      • Object Detection
    • Segmentation
      • Semantic Segmentation
      • Instance Segmentation
    • Anomaly Detection
    • Action Recognition
      • Action Classification
      • Action Detection
    • Visual Prompting
      • Visual Prompting (Fine-tuning)
      • Visual Prompting (Zero-shot learning)
  • Additional Features
    • Models Optimization
    • Hyperparameters Optimization
    • Auto-configuration
    • Adaptive Training
    • Explainable AI (XAI)
    • Noisy Label Detection
    • Fast Data Loading
    • Improve Small Object Detection with Image Tiling
    • Configurable Input Size
    • Augmentations per model

Reference

  • API reference
    • otx
      • otx.algorithms
        • otx.algorithms.action
        • otx.algorithms.anomaly
        • otx.algorithms.classification
        • otx.algorithms.common
        • otx.algorithms.detection
        • otx.algorithms.segmentation
        • otx.algorithms.visual_prompting
      • otx.api
        • otx.api.configuration
        • otx.api.entities
        • otx.api.serialization
        • otx.api.usecases
        • otx.api.utils
      • otx.cli
        • otx.cli.builder
        • otx.cli.manager
        • otx.cli.registry
        • otx.cli.tools
        • otx.cli.utils
      • otx.core
        • otx.core.data
        • otx.core.file
        • otx.core.ov
        • otx.core.patcher
      • otx.hpo
      • otx.recipes
        • otx.recipes.stages
      • otx.utils
        • otx.utils.logger
        • otx.utils.utils

Release Notes

  • Releases

otx.algorithms.common.adapters.torch.amp#

Custom AMP (Automatic Mixed Precision package) in OTX.

Modules

otx.algorithms.common.adapters.torch.amp.xpu_grad_scaler

Custom GradScaler to scale loss.

previous

otx.algorithms.common.adapters.torch

next

otx.algorithms.common.adapters.torch.amp.xpu_grad_scaler
Show Source

© Copyright 2024, OpenVINO™ Training Extensions Contributors.

Built with the PyData Sphinx Theme 0.12.0.

Created using Sphinx 7.2.6.