Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Yolov5 | 44,755 | 2 | 5 months ago | 3 | June 08, 2022 | 144 | agpl-3.0 | Python | ||
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite | ||||||||||
Netron | 26,674 | 4 | 70 | 8 days ago | 610 | December 09, 2023 | 27 | mit | JavaScript | |
Visualizer for neural network, deep learning and machine learning models | ||||||||||
Ncnn | 19,560 | 1 | 16 days ago | 26 | October 27, 2023 | 1,010 | other | C++ | ||
ncnn is a high-performance neural network inference framework optimized for the mobile platform | ||||||||||
Onnx | 16,275 | 148 | 493 | 5 months ago | 31 | October 26, 2023 | 296 | apache-2.0 | Python | |
Open standard for machine learning interoperability | ||||||||||
Onnxruntime | 12,957 | 8 | 88 | a month ago | 39 | November 20, 2023 | 2,222 | mit | C++ | |
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator | ||||||||||
Clip As Service | 12,268 | 17 | 14 | 5 months ago | 56 | December 20, 2019 | 293 | other | Python | |
🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP | ||||||||||
Yolov3 | 9,865 | 5 months ago | 6 | agpl-3.0 | Python | |||||
YOLOv3 in PyTorch > ONNX > CoreML > TFLite | ||||||||||
Yolox | 8,759 | 1 | 6 months ago | 3 | April 22, 2022 | 705 | apache-2.0 | Python | ||
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/ | ||||||||||
Burn | 7,015 | 8 | 2 months ago | 15 | December 04, 2023 | 145 | apache-2.0 | Rust | ||
Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals. | ||||||||||
Efficientnet Pytorch | 6,577 | 7 | 36 | 3 years ago | 13 | April 15, 2021 | 133 | apache-2.0 | Python | |
A PyTorch implementation of EfficientNet and EfficientNetV2 (coming soon!) |