README.md

Easily train or fine-tune SOTA computer vision models from one training repository.

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SuperGradients

Introduction

Welcome to SuperGradients, a free open-source training library for PyTorch-based deep learning models. SuperGradients allows you to train models of any computer vision tasks or import pre-trained SOTA models, such as object detection, classification of images, and semantic segmentation for videos and images.

Whether you are a beginner or an expert it is likely that you already have your own training script, model, loss function implementation, etc., and thus you have experienced how difficult it is to develop a production-ready deep learning model, the overhead of integrating with existing training tools with very different and stiff formats and conventions, how much effort it takes to find a suitable architecture for your needs when every repo is focusing on just one task.

With SuperGradients you can:

• Train models for any Computer Vision task or import production-ready pre-trained SOTA models (detection, segmentation, and classification - YOLOv5, DDRNet, EfficientNet, RegNet, ResNet, MobileNet, etc.)
• Shorten the training process using tested and proven recipes & code examples
• Easily configure your own or use plug&play training, dataset, and architecture parameters.
• Save time and easily integrate it into your codebase.

Documentation

Check SuperGradients Docs for full documentation, user guide, and examples.

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Table of Content:

• Getting Started
  • Quick Start Notebook
  • Walkthrough Notebook
• Installation Methods
  • Prerequisites
  • Quick Installation of stable version
  • Installing from GitHub
• Computer Vision Models' Pretrained Checkpoints
  • Pretrained Classification PyTorch Checkpoints
  • Pretrained Object Detection PyTorch Checkpoints
  • Pretrained Semantic Segmentation PyTorch Checkpoints
• Contributing
• Citation
• Community
• License

Getting Started

Quick Start Notebook

Get started with our quick start notebook on Google Colab for a quick and easy start using free GPU hardware

SuperGradients Quick Start in Google Colab

Download notebook

View source on GitHub

SuperGradients Walkthrough Notebook

Learn more about SuperGradients training components with our walkthrough notebook on Google Colab for an easy to use tutorial using free GPU hardware

SuperGradients Walkthrough in Google Colab

Download notebook

View source on GitHub

Installation Methods

Prerequisites

General requirements:

• Python 3.7, 3.8 or 3.9 installed.
• torch>=1.9.0
  • https://pytorch.org/get-started/locally/
• The python packages that are specified in requirements.txt;

To train on nvidia GPUs:

• Nvidia CUDA Toolkit >= 11.2
• CuDNN >= 8.1.x
• Nvidia Driver with CUDA >= 11.2 support (≥460.x)

Quick Installation of stable version

See in PyPi

pip install super-gradients

That's it !

Installing from GitHub

pip install git+https://github.com/Deci-AI/super-gradients.git@stable

Computer Vision Models' Pretrained Checkpoints

Pretrained Classification PyTorch Checkpoints

Model	Dataset	Resolution	Top-1	Top-5	Latency b1T4	Throughput b1T4
EfficientNet B0	ImageNet	224x224	77.62	93.49	1.16ms	862fps
RegNetY200	ImageNet	224x224	70.88	89.35	1.07ms	928.3fps
RegNetY400	ImageNet	224x224	74.74	91.46	1.22ms	816.5fps
RegNetY600	ImageNet	224x224	76.18	92.34	1.19ms	838.5fps
RegNetY800	ImageNet	224x224	77.07	93.26	1.18ms	841.4fps
ResNet18	ImageNet	224x224	70.6	89.64	0.599ms	1669fps
ResNet34	ImageNet	224x224	74.13	91.7	0.89ms	1123fps
ResNet50	ImageNet	224x224	76.3	93.0	0.94ms	1063fps
MobileNetV3_large-150 epochs	ImageNet	224x224	73.79	91.54	0.87ms	1149fps
MobileNetV3_large-300 epochs	ImageNet	224x224	74.52	91.92	0.87ms	1149fps
MobileNetV3_small	ImageNet	224x224	67.45	87.47	0.75ms	1333fps
MobileNetV2_w1	ImageNet	224x224	73.08	91.1	0.58ms	1724fps

NOTE: Performance measured on T4 GPU with TensorRT, using FP16 precision and batch size 1

Pretrained Object Detection PyTorch Checkpoints

Model	Dataset	Resolution	mAPval 0.5:0.95	Latency b1T4	Throughput b64T4
YOLOv5 nano	COCO	640x640	27.7	6.55ms	177.62fps
YOLOv5 small	COCO	640x640	37.3	7.13ms	159.44fps
YOLOv5 medium	COCO	640x640	45.2	8.95ms	121.78fps
YOLOv5 large	COCO	640x640	48.0	11.49ms	95.99fps

NOTE: Performance measured on T4 GPU with TensorRT, using FP16 precision and batch size 1 (latency) and batch size 64 (througput)

Pretrained Semantic Segmentation PyTorch Checkpoints

Model	Dataset	Resolution	mIoU	Latency b1T4	Throughput b64T4
DDRNet23	Cityscapes	1024x2048	78.65	25.48ms	37.4fps
DDRNet23 slim	Cityscapes	1024x2048	76.6	22.24ms	45.7fps
ShelfNet_LW_34	COCO Segmentation (21 classes from PASCAL including background)	512x512	65.1	-	-

NOTE: Performance measured on T4 GPU with TensorRT, using FP16 precision and batch size 1 (latency) and batch size 64 (througput)

Contributing

To learn about making a contribution to SuperGradients, please see our Contribution page.

Our awesome contributors:

Made with contrib.rocks.

Citation

If you are using SuperGradients library or benchmarks in your research, please cite SuperGradients deep learning training library.

Community

If you want to be a part of SuperGradients growing community, hear about all the exciting news and updates, need help, request for advanced features, or want to file a bug or issue report, we would love to welcome you aboard!

• Slack is the place to be and ask questions about SuperGradients and get support. Click here to join our Slack

• To report a bug, file an issue on GitHub.

• You can also join the community mailing list to ask questions about the project and receive announcements.

• For a shorth meeting with SuperGradients PM, use this link and choose your prefered time.

License

This project is released under the Apache 2.0 license.