Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Pytorch | 64,175 | 146 | 4 hours ago | 23 | August 10, 2022 | 11,410 | other | C++ | ||
Tensors and Dynamic neural networks in Python with strong GPU acceleration | ||||||||||
Tensorflow Examples | 42,312 | 5 months ago | 218 | other | Jupyter Notebook | |||||
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2) | ||||||||||
Pytorch Tutorial | 25,860 | 11 days ago | 88 | mit | Python | |||||
PyTorch Tutorial for Deep Learning Researchers | ||||||||||
Darknet | 23,942 | 17 hours ago | 1,948 | other | C | |||||
Convolutional Neural Networks | ||||||||||
Ml From Scratch | 21,618 | 5 months ago | 4 | June 17, 2017 | 48 | mit | Python | |||
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning. | ||||||||||
Plotneuralnet | 18,647 | 3 months ago | 76 | mit | TeX | |||||
Latex code for making neural networks diagrams | ||||||||||
Awesome Tensorflow | 16,809 | 3 months ago | 30 | cc0-1.0 | ||||||
TensorFlow - A curated list of dedicated resources http://tensorflow.org | ||||||||||
Openface | 14,540 | 5 months ago | 13 | apache-2.0 | Lua | |||||
Face recognition with deep neural networks. | ||||||||||
Pwc | 14,522 | 3 years ago | 22 | |||||||
Papers with code. Sorted by stars. Updated weekly. | ||||||||||
Deeplearning_ai_books | 14,417 | a year ago | 53 | HTML | ||||||
deeplearning.ai(吴恩达老师的深度学习课程笔记及资源) |
Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.
Discord invite link for for communication and questions: https://discord.gg/zSq8rtW
paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors: https://arxiv.org/abs/2207.02696
source code - Pytorch (use to reproduce results): WongKinYiu/yolov7
Official YOLOv7 is more accurate and faster than YOLOv5 by 120% FPS, than YOLOX by 180% FPS, than Dual-Swin-T by 1200% FPS, than ConvNext by 550% FPS, than SWIN-L by 500% FPS.
YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100, batch=1.
+500%
FPS faster than SWIN-L Cascade-Mask R-CNN (53.9% AP, 9.2 FPS A100 b=1)+550%
FPS faster than ConvNeXt-XL C-M-RCNN (55.2% AP, 8.6 FPS A100 b=1)+120%
FPS faster than YOLOv5-X6-r6.1 (55.0% AP, 38 FPS V100 b=1)+1200%
FPS faster than Dual-Swin-T C-M-RCNN (53.6% AP, 6.5 FPS V100 b=1)+150%
FPS faster than PPYOLOE-X (51.9% AP, 45 FPS V100 b=1)+180%
FPS faster than YOLOX-X (51.1% AP, 58 FPS V100 b=1)paper (CVPR 2021): https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html
source code - Pytorch (use to reproduce results): WongKinYiu/ScaledYOLOv4
source code - Darknet: AlexeyAB/darknet
source code: AlexeyAB/darknet
For more information see the Darknet project website.
https://paperswithcode.com/sota/object-detection-on-coco
AP50:95 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2011.08036
@misc{https://doi.org/10.48550/arxiv.2207.02696,
doi = {10.48550/ARXIV.2207.02696},
url = {https://arxiv.org/abs/2207.02696},
author = {Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
@misc{bochkovskiy2020yolov4,
title={YOLOv4: Optimal Speed and Accuracy of Object Detection},
author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao},
year={2020},
eprint={2004.10934},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@InProceedings{Wang_2021_CVPR,
author = {Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
title = {{Scaled-YOLOv4}: Scaling Cross Stage Partial Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {13029-13038}
}