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


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 experienced with 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 is 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.


Table of Content:

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:

Quick Installation of stable version

Not yet avilable 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

Throughout 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

Throughout 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

Throughout 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 use SuperGradients library or benchmark 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!

License

This project is released under the Apache 2.0 license.