Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
YOLOv3、YOLOv4、YOLOv5、YOLOv5-Lite、YOLOv6-v1、YOLOv6-v2、YOLOv7、YOLOX、YOLOX-Lite、PP-YOLOE、PP-PicoDet-Plus、YOLO-Fastest v2、FastestDet、YOLOv5-SPD、TensorRT、NCNN、Tengine、OpenVINO
| Date | Stars |
|---|---|
| 2026-07-24 | 768 |
| 2026-07-25 | 768 |
| 2026-07-28 | 767 |
| 2026-07-30 | 767 |
| 2026-08-06 | 767 |
Today
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Momentum
0.0
growth rate 0.00%/day
# YOLOU：United, Study and easier to Deploy The purpose of our creation of YOLOU is to better learn the algorithms of the YOLO series and pay tribute to our predecessors. Here "U" means United, mainly to gather more algorithms about the YOLO series through this project, so that friends can better learn the knowledge of object detection. At the same time, in order to better apply AI technology, YOLOU will also join The corresponding Deploy technology will accelerate the implementation of the algorithms we have learned and realize the value.  At present, the YOLO series algorithms mainly included in YOLOU are: **Anchor-base**: [YOLOv3](https://github.com/ultralytics/yolov3), [YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4), [YOLOv5](https://github.com/ultralytics/yolov5), [YOLOv5-Lite](https://github.com/ppogg/YOLOv5-Lite), [YOLOv7](https://github.com/WongKinYiu/yolov7), [YOLOv5-TPH](https://github.com/cv516Buaa/tph-yolov5), [YOLO-Fastest v2](https://github.com/dog-qiuqiu/Yolo-FastestV2), [YOLO-LF](https://github.com/h72001346/yolosav1), [YOLO-SA](https://github.com/h72001346/yolosav1), [YOLOR](https://github.com/WongKinYiu/yolor), [YOLOv5-SPD](https://github.com/LabSAINT/SPD-Conv) **Anchor-Free**: [YOLOv6-v1](https://github.com/meituan/YOLOv6/releases/tag/0.1.0), [YOLOv6-v2](https://github.com/meituan/YOLOv6/releases/tag/0.2.0), [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX), [YOLOE](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe), [YOLOX-Lite](https://github.com/jizhishutong/YOLOU), [FastestDet](https://github.com/dog-qiuqiu/FastestDet) **Face-Detection**: [YOLOv5-Face](https://github.com/deepcam-cn/yolov5-face), [YOLOFace-v2](https://github.com/Krasjet-Yu/YOLO-FaceV2) **Segmentation**: [YOLOv5-Segment](https://github.com/ultralytics/yolov5) **KeyPoint**: [YOLOv7-Keypoint](https://github.com/WongKinYiu/yolov7) **Classfication**: ResNet, DarkNet,...... <details open> <summary>Comparison of ablation experiment results</summary> | Model | size(pixels) | [email protected] | [email protected]:95 | Parameters(M) | GFLOPs | TensorRT-FP32(b16)<br>ms/fps | TensorRT-FP16(b16)<br/>ms/fps | |:------------------------------------------------------------------------------------------------|:------------:| :-------: | :-------: | :-----------: | :----: | :--------------------------: | :---------------------------: | | [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n.pt) | 640 | 45.7 | 28.0 | 1.9 | 4.5 | 0.95/1054.64 | 0.61/1631.64 | | [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt) | 640 | 56.8 | 37.4 | 7.2 | 16.5 | 1.7/586.8 | 0.84/1186.42 | | [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m.pt) | 640 | 64.1 | 45.4 | 21.2 | 49.0 | 4.03/248.12 | 1.42/704.20 | | [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l.pt) | 640 | 67.3 | 49.0 | 46.5 | 109.1 | | | | [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x.pt) | 640 | 68.9 | 50.7 | 86.7 | 205.7 | | | | YOLOv6-T | 640 | | | | | | | | YOLOv6-n | 640 | |
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2980fa1814cca0c0, topic:yolo, readme:object detection
matched fp:2980fa1814cca0c0, topic:tensorrt