SuperGradients
Project description
Easily train or fine-tune SOTA computer vision models with one open source training library
Website • Why Use SG? • User Guide • Docs • SOTA Pretrained Models • Community • License • Deci Lab
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.
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.
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 - Classification
Get started with our quick start notebook for image classification tasks on Google Colab for a quick and easy start using free GPU hardware.
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Quick Start Notebook - Object Detection
Get started with our quick start notebook for object detection tasks on Google Colab for a quick and easy start using free GPU hardware.
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SuperGradients Complete 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
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Transfer Learning with SG Notebook - Object Detection
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
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Installation Methods
Prerequisites
General requirements
- Python 3.7, 3.8 or 3.9 installed.
- torch>=1.9.0
- 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
Install using GitHub
pip install git+https://github.com/Deci-AI/super-gradients.git@stable
Documentation
Check SuperGradients Docs for full documentation, user guide, and examples.
Computer Vision Models - Pretrained Checkpoints
Pretrained Classification PyTorch Checkpoints
Model | Dataset | Resolution | Top-1 | Top-5 | Latency b1T4 | Throughput b1T4 |
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EfficientNet B0 | ImageNet | 224x224 | 77.62 | 93.49 | 1.16ms | 862fps |
RegNet Y200 | ImageNet | 224x224 | 70.88 | 89.35 | 1.07ms | 928.3fps |
RegNet Y400 | ImageNet | 224x224 | 74.74 | 91.46 | 1.22ms | 816.5fps |
RegNet Y600 | ImageNet | 224x224 | 76.18 | 92.34 | 1.19ms | 838.5fps |
RegNet Y800 | ImageNet | 224x224 | 77.07 | 93.26 | 1.18ms | 841.4fps |
ResNet 18 | ImageNet | 224x224 | 70.6 | 89.64 | 0.599ms | 1669fps |
ResNet 34 | ImageNet | 224x224 | 74.13 | 91.7 | 0.89ms | 1123fps |
ResNet 50 | ImageNet | 224x224 | 79.47 | 93.0 | 0.94ms | 1063fps |
MobileNet V3_large-150 epochs | ImageNet | 224x224 | 73.79 | 91.54 | 0.87ms | 1149fps |
MobileNet V3_large-300 epochs | ImageNet | 224x224 | 74.52 | 91.92 | 0.87ms | 1149fps |
MobileNet V3_small | ImageNet | 224x224 | 67.45 | 87.47 | 0.75ms | 1333fps |
MobileNet V2_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 |
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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 |
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DDRNet 23 | Cityscapes | 1024x2048 | 78.65 | 7.62ms | 131.3fps | 25.94ms |
DDRNet 23 slim | Cityscapes | 1024x2048 | 76.6 | 3.56ms | 280.5fps | 22.80ms |
STDC 1-Seg50 | Cityscapes | 512x1024 | 74.36 | 2.83ms | 353.3fps | 12.57ms |
STDC 1-Seg75 | Cityscapes | 768x1536 | 76.87 | 5.71ms | 175.1fps | 26.70ms |
STDC 2-Seg50 | Cityscapes | 512x1024 | 75.27 | 3.74ms | 267.2fps | 13.89ms |
STDC 2-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!
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Slack is the place to be and ask questions about SuperGradients and get support. Click here to join our Slack
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To report a bug, file an issue on GitHub.
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Join the SG Newsletter for staying up to date with new features and models, important announcements, and upcoming events.
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For a short meeting with us, use this link and choose your preferred time.
License
This project is released under the Apache 2.0 license.
Deci Lab
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Features:
- Automatically compile and quantize your models with just a few clicks (TrT, OpenVino).
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- Easily benchmark your models’ performance on different hardware and batch sizes.
- Invite co-workers to collaborate on models and communicate your progress.
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