Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Supervision | 9,320 | 57 | 4 months ago | 43 | December 08, 2023 | 74 | mit | Python | ||
We write your reusable computer vision tools. 💜 | ||||||||||
Ml Cvnets | 1,543 | 7 months ago | 33 | other | Python | |||||
CVNets: A library for training computer vision networks | ||||||||||
Efficientdet.pytorch | 1,408 | 3 years ago | 114 | mit | Python | |||||
Implementation EfficientDet: Scalable and Efficient Object Detection in PyTorch | ||||||||||
Torchdistill | 1,171 | 2 | 4 months ago | 24 | November 06, 2023 | mit | Python | |||
A coding-free framework built on PyTorch for reproducible deep learning studies. 🏆22 knowledge distillation methods presented at CVPR, ICLR, ECCV, NeurIPS, ICCV, etc are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark. | ||||||||||
Deeplabv3plus Pytorch | 1,010 | 2 years ago | 51 | mit | Python | |||||
Pretrained DeepLabv3 and DeepLabv3+ for Pascal VOC & Cityscapes | ||||||||||
Pytorch Auto Drive | 713 | 8 months ago | 30 | bsd-3-clause | Python | |||||
PytorchAutoDrive: Segmentation models (ERFNet, ENet, DeepLab, FCN...) and Lane detection models (SCNN, RESA, LSTR, LaneATT, BézierLaneNet...) based on PyTorch with fast training, visualization, benchmarking & deployment help | ||||||||||
Deeplab Pytorch | 668 | 4 years ago | 7 | mit | Python | |||||
PyTorch implementation of DeepLab v2 on COCO-Stuff / PASCAL VOC | ||||||||||
Pytorch Deeplab Resnet | 598 | 9 months ago | 14 | mit | Python | |||||
DeepLab resnet v2 model in pytorch | ||||||||||
Espnetv2 | 375 | 3 years ago | mit | Python | ||||||
A light-weight, power efficient, and general purpose convolutional neural network | ||||||||||
Edgenets | 340 | 3 years ago | 2 | mit | Python | |||||
This repository contains the source code of our work on designing efficient CNNs for computer vision |