Easily train or fine-tune SOTA computer vision models with one open source training library Tweet


SuperGradients

Introduction

Welcome to SuperGradients, a free, open-source training library for PyTorch-based deep learning models. SuperGradients allows you to train or fine-tune SOTA pre-trained models for all the most commonly applied computer vision tasks with just one training library. We currently support object detection, image classification and semantic segmentation for videos and images.

Docs and full user guide

Why use SuperGradients?

Built-in SOTA Models

Easily load and fine-tune production-ready, pre-trained SOTA models that incorporate best practices and validated hyper-parameters for achieving best-in-class accuracy.

Easily Reproduce our Results

Why do all the grind work, if we already did it for you? leverage tested and proven recipes & code examples for a wide range of computer vision models generated by our team of deep learning experts. Easily configure your own or use plug & play hyperparameters for training, dataset, and architecture.

Production Readiness and Ease of Integration

All SuperGradients models’ are production ready in the sense that they are compatible with deployment tools such as TensorRT (Nvidia) and OpenVino (Intel) and can be easily taken into production. With a few lines of code you can easily integrate the models into your codebase.

Documentation

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

What’s New

  • 【07/02/2022】 We added RegSeg recipes and pre-trained models to our Semantic Segmentation models.

  • 【01/02/2022】 We added issue templates for feature requests and bug reporting.

  • 【20/01/2022】 STDC family - new recipes added with even higher mIoU💪

  • 【17/01/2022】 We have released transfer learning example notebook for object detection (YOLOv5).

Check out SG full release notes.

Comming soon

  • [ ] YOLOX models (recipes, pre-trained checkpoints).

  • [ ] SSD MobileNet models (recipes, pre-trained checkpoints) for edge devices deployment.

  • [ ] Transfer learning example notebook for semantic segmentation (STDC).

  • [ ] Dali implementation.

  • [ ] Integration with professional tools.


Table of Content

See Table

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


Transfer Learning with SG Notebook

Learn more about SuperGradients transfer learning or fine tuning abilities with our COCO pre-trained YoloV5nano fine tuning into a sub-dataset of PASCAL VOC example notebook on Google Colab for an easy to use tutorial using free GPU hardware

SuperGradients Transfer Learning 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

Install stable version using PyPi

See in PyPi

pip install super-gradients

That’s it !

Install using 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

79.47

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 (throughput)

Pretrained Semantic Segmentation PyTorch Checkpoints

Model

Dataset

Resolution

mIoU

Latency b1T4

Throughput b1T4

Latency b1T4 including IO

DDRNet23

Cityscapes

1024x2048

78.65

7.62ms

131.3fps

25.94ms

DDRNet23 slim

Cityscapes

1024x2048

76.6

3.56ms

280.5fps

22.80ms

STDC1-Seg50

Cityscapes

512x1024

74.36

2.83ms

353.3fps

12.57ms

STDC1-Seg75

Cityscapes

768x1536

76.87

5.71ms

175.1fps

26.70ms

STDC2-Seg50

Cityscapes

512x1024

75.27

3.74ms

267.2fps

13.89ms

STDC2-Seg75

Cityscapes

768x1536

78.93

7.35ms

135.9fps

28.18ms

RegSeg (exp48)

Cityscapes

1024x2048

78.15

13.09ms

76.4fps

41.88ms

Larger RegSeg (exp53)

Cityscapes

1024x2048

79.2

24.82ms

40.3fps

51.87ms

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 not including IO

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 short meeting with SuperGradients PM, use this link and choose your preferred time.

License

This project is released under the Apache 2.0 license.


Deci Lab

Deci Lab supports all common frameworks and Hardware, from Intel CPUs to Nvidia’s GPUs and Jetsons

You can enjoy immediate improvement in throughput, latency, and memory with the Deci Lab. It optimizes deep learning models using best-of-breed technologies, such as quantization and graph compilers.

Get a complete benchmark of your models’ performance on different hardware and batch sizes in a single interface. Invite co-workers to collaborate on models and communicate your progress.

Sign up for Deci Lab for free here