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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.
MobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB:fire::fire::fire:
| Date | Stars |
|---|---|
| 2026-07-24 | 1742 |
| 2026-07-25 | 1742 |
| 2026-07-28 | 1743 |
| 2026-07-30 | 1743 |
| 2026-08-06 | 1743 |
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## 2021.2.6 此项目不再更新，新项目地址: Yolo-Fastest： Faster and stronger https://github.com/dog-qiuqiu/Yolo-Fastest  * ***Yolo-Fastest： Faster and stronger https://github.com/dog-qiuqiu/Yolo-Fastest*** * ***此表中NCNN基准未更新最新ARM82数据，最新版本NCNN理论上ARM82会有一倍速度提升，待更新...*** * 添加基于ncnn的106关键点 C sample:https://github.com/dog-qiuqiu/MobileNet-Yolo/tree/master/sample/ncnn ## ***Darknet Group convolution is not well supported on some GPUs such as NVIDIA PASCAL!!! * https://github.com/AlexeyAB/darknet/issues/6091#issuecomment-651667469 ## 针对某些Pascal显卡例如1080ti在darknet上 训练失败/训练异常缓慢/推理速度异常 的可以采用Pytorch版yolo3框架 训练/推理 * https://github.com/dog-qiuqiu/yolov3 ## MobileNetV2-YOLOv3-Lite&Nano Darknet #### Mobile inference frameworks benchmark (4*ARM_CPU) Network|VOC mAP(0.5)|COCO mAP(0.5)|Resolution|Inference time (NCNN/Kirin 990)|Inference time (MNN arm82/Kirin 990)|FLOPS|Weight size :---:|:---:|:---:|:---:|:---:|:---:|:---:|:---: [MobileNetV2-YOLOv3-Lite](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/tree/master/MobileNetV2-YOLOv3-Lite)(our)|73.26|37.44|320|28.42 ms|18 ms|1.8BFlops|8.0MB [MobileNetV2-YOLOv3-Nano](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/tree/master/MobileNetV2-YOLOv3-Nano)(our)|65.27|30.13|320|10.16 ms|5 ms|0.5BFlops|3.0MB [MobileNetV2-YOLOv3](https://github.com/eric612/MobileNet-YOLO)|70.7|&|352|32.15 ms|& ms|2.44BFlops|14.4MB [MobileNet-SSD](https://github.com/chuanqi305/MobileNet-SSD)|72.7|&|300|26.37 ms|& ms|& BFlops|23.1MB [YOLOv5s](https://github.com/ultralytics/yolov5)|&|56.2|416|150.5 ms|& ms|13.2BFlops|28.1MB [YOLOv3-Tiny-Prn](https://github.com/AlexeyAB/darknet#pre-trained-models)|&|33.1|416|36.6 ms|& ms|3.5BFlops|18.8MB [YOLOv4-Tiny](https://github.com/AlexeyAB/darknet#pre-trained-models)|&|40.2|416|44.6 ms|& ms|6.9BFlops|23.1MB [YOLO-Nano](https://github.com/liux0614/yolo_nano)|69.1|&|416|& ms|& ms|4.57BFlops|4.0MB * Support mobile inference frameworks such as NCNN&MNN * The mnn benchmark only includes the forward inference time * The ncnn benchmark is the forward inference time + post-processing time(NMS...) of the convolution feature map. * Darknet Train Configuration: CUDA-version: 10010 (10020), cuDNN: 7.6.4,OpenCV version: 4 GPU:RTX2080ti ## MobileNetV2-YOLOv3-Lite-COCO Test results  # Application ## Ultralight-SimplePose * A ultra-lightweight human body posture key point prediction model designed for mobile devices, which can cooperate with MobileNetV2-YOLOv3-Nano to complete the human body posture estimation task * https://github.com/dog-qiuqiu/Ultralight-SimplePose  ## YoloFace-500k: 500kb yolo-Face-Detection Network|Resolution|Inference time (NCNN/Kirin 990)|Inference time (MNN arm82/Kirin 990)|FLOPS|Weight size :---:|:---:|:---:|:---:|:---:|:---: UltraFace-version-RFB|320x240|&ms|3.36ms|0.1BFlops|1.3MB UltraFace-version-Slim|320x240|&ms|3.06ms|0.1BFlops|1.2MB [yoloface-500k](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/tree/master/yoloface-500k/v1)|320x256|5.5ms|2.4ms|0.1BFlops|0.52MB [yoloface-500k-v2](https://github.com/dog-qiuqiu/MobileNetv2-YOLOV3/tree/master/yoloface-500k/v2)|352x288|4.7ms|&ms|0.1BFlops|0.42MB * 都500k了，要啥mAP:sunglasses: * Inference time (DarkNet/i7-6700):13ms * The mnn benchmark only includes the forward inference time * The ncnn benchmark is the forward inference time + post-processing time(NMS...) of the convolution feature map. ## Wider Face Val Model|Easy Set|Medium Set|Hard Set ------|--------|----------|-------- libfacedetection v1（caffe）|0.65 |0.5 |0.233 libfacedetection v2（caffe）|0.714 |0.585 |0.306 Retinaface-Mobilenet-0.25 (Mxnet) |0.745|0.553|0.232 version-slim-320|0.77 |0.671 |0.395 version-RFB-320|0.787 |0.698 |0.438 [yoloface-500k-320](https:
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:af0551823608ed7f, topic:computer-vision, topic:object-detection, topic:yolo