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Overview
SuperGradients
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latest
latest
Welcome
Welcome
Intro
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Model Zoo
Quick Start
Quick Start
Basic
Classification
Object Detection
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Pose Estimation
Training an external model
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Custom training Setup
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Main Components
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YAMLs and Recipes
YAMLs and Recipes
Configurations
Training
Factories
Custom Recipes
Checkpoints
Docker
Output Adapter
Features
Features
Training Modes
Logging
Experiment Monitoring
Exponential Moving Average (EMA)
Automatic Mixed Precision (AMP)
Knowledge Distillation (KD)
Quantization (PTQ & QAT)
Model Export (ONNX & TensorRT)
Troubleshooting
Contribution
Code
Code
training
training
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Kd trainer
Legacy
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Metrics
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Params
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Pre launch callbacks
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Processing
Qat trainer
Sg trainer
Training hyperparams
Transforms
Utils
modules
modules
All detection modules
Anti alias
Base modules
Conv bn act block
Conv bn relu block
Detection modules
Head replacement utils
Multi output modules
Pixel shuffle
Pose estimation modules
Qarepvgg block
Quantization
Repvgg block
Sampling
Se blocks
Skip connections
Utils
common
common
Abstractions
Auto logging
Aws connection
Crash handler
Data connection
Data interface
Data types
Decorators
Deprecate
Environment
Exceptions
Factories
Object names
Plugins
Registry
Sg loggers
V3.2
V3.2
Welcome
Welcome
Intro
Installation
Model Zoo
Quick Start
Quick Start
Basic
Classification
Object Detection
Segmentation
Pose Estimation
Training an external model
Pretrained Model Prediction
Pretrained Model Prediction
Prediction
Custom training Setup
Main Components
Main Components
Models
Dataset
Dataset
Data
Computer Vision Datasets
Loss functions
LR schedulers
Metrics
Optimizers
Phase Callbacks
YAMLs and Recipes
YAMLs and Recipes
Configurations
Recipes
Checkpoints
Docker
Output Adapter
Features
Features
Training Modes
Logging
Experiment Monitoring
Exponential Moving Average (EMA)
Automatic Mixed Precision (AMP)
Knowledge Distillation (KD)
Quantization (PTQ & QAT)
Troubleshooting
Contribution
Code
Code
training
training
Dataloaders
Datasets
Kd trainer
Legacy
Losses
Metrics
Models
Params
Pipelines
Pre launch callbacks
Processing
Qat trainer
Sg trainer
Training hyperparams
Transforms
Utils
Pretrained models
modules
modules
All detection modules
Anti alias
Base modules
Conv bn act block
Conv bn relu block
Detection modules
Head replacement utils
Multi output modules
Pixel shuffle
Pose estimation modules
Qarepvgg block
Quantization
Repvgg block
Sampling
Se blocks
Skip connections
Utils
common
common
Abstractions
Auto logging
Aws connection
Crash handler
Data connection
Data interface
Data types
Decorators
Environment
Exceptions
Factories
Plugins
Registry
Sg loggers
Object names
V3.1
V3.1
Welcome
Welcome
Intro
Installation
Model Zoo
Quick Start
Quick Start
Basic
Classification
Object Detection
Segmentation
Pose Estimation
Training an external model
Pretrained Model Prediction
Pretrained Model Prediction
Prediction
Custom training Setup
Main Components
Main Components
Models
Dataset
Dataset
Data
Computer Vision Datasets
Loss functions
LR schedulers
Metrics
Optimizers
Phase Callbacks
YAMLs and Recipes
YAMLs and Recipes
Configurations
Recipes
Checkpoints
Docker
Output Adapter
Features
Features
Training Modes
Logging
Experiment Monitoring
Exponential Moving Average (EMA)
Automatic Mixed Precision (AMP)
Knowledge Distillation (KD)
Quantization (PTQ & QAT)
Troubleshooting
Contribution
Code
Code
training
training
Dataloaders
Datasets
Kd trainer
Legacy
Losses
Metrics
Models
Pipelines
Pre launch callbacks
Processing
Qat trainer
Qat trainer
Table of contents
V3_1.src.super_gradients.training.qat_trainer.qat_trainer
Sg trainer
Training hyperparams
Transforms
Utils
Exceptions
Params
Pretrained models
modules
modules
All detection modules
Anti alias
Base modules
Conv bn act block
Conv bn relu block
Detection modules
Head replacement utils
Multi output modules
Pixel shuffle
Pose estimation modules
Qarepvgg block
Quantization
Repvgg block
Sampling
Se blocks
Skip connections
Utils
common
common
Abstractions
Auto logging
Aws connection
Crash handler
Data connection
Data interface
Data types
Decorators
Environment
Exceptions
Factories
Plugins
Registry
Sg loggers
Object names
DataGradients
DataGradients
Welcome
Welcome
Intro
Feature Description
Feature Configuration
Platform SDK
Platform SDK
Welcome
Tutorials
Tutorials
Installation & Setup
Model Optimization Quickstart
PyTorch Models - Hugging Face & SuperGradients
Code Reference
Code Reference
User Operations
Model Registry
Benchmark and Optimization
AutoNAC and Experiment Management
Infery
Infery
Welcome
Quick Start
Quick Start
Installation
Analysis
Compilation
Inference
Serving
Parameters
Command Line
Table of contents
V3_1.src.super_gradients.training.qat_trainer.qat_trainer
Qat trainer