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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.
🍅🍅🍅YOLOv5-Lite: Evolved from yolov5 and the size of model is only 900+kb (int8) and 1.7M (fp16). Reach 15 FPS on the Raspberry Pi 4B~
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| 2026-07-30 | 2488 |
| 2026-08-07 | 2489 |
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| 2026-10-24 | 2488 |
| 2026-10-31 | 2489 |
| 2026-11-01 | 2489 |
| 2026-11-04 | 2489 |
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# YOLOv5-Lite：Lighter, faster and easier to deploy   Perform a series of ablation experiments on yolov5 to make it lighter (smaller Flops, lower memory, and fewer parameters) and faster (add shuffle channel, yolov5 head for channel reduce. It can infer at least 10+ FPS On the Raspberry Pi 4B when input the frame with 320×320) and is easier to deploy (removing the Focus layer and four slice operations, reducing the model quantization accuracy to an acceptable range).  ## Comparison of ablation experiment results ID|Model | Input_size|Flops| Params | Size（M） |[email protected]|[email protected]:0.95 :-----:|:-----:|:-----:|:----------:|:----:|:----:|:----:|:----:| 001| yolo-fastest| 320×320|0.25G|0.35M|1.4| 24.4| - 002| YOLOv5-Lite<sub>e</sub><sup>ours</sup>|320×320|0.73G|0.78M|1.7| 35.1|-| 003| NanoDet-m| 320×320| 0.72G|0.95M|1.8|- |20.6 004| yolo-fastest-xl| 320×320|0.72G|0.92M|3.5| 34.3| - 005| YOLOX<sub>Nano</sub>|416×416|1.08G|0.91M|7.3(fp32)| -|25.8| 006| yolov3-tiny| 416×416| 6.96G|6.06M|23.0| 33.1|16.6 007| yolov4-tiny| 416×416| 5.62G|8.86M| 33.7|40.2|21.7 008| YOLOv5-Lite<sub>s</sub><sup>ours</sup>| 416×416|1.66G |1.64M|3.4| 42.0|25.2 009| YOLOv5-Lite<sub>c</sub><sup>ours</sup>| 512×512|5.92G |4.57M|9.2| 50.9|32.5| 010| NanoDet-EfficientLite2| 512×512| 7.12G|4.71M|18.3|- |32.6 011| YOLOv5s(6.0)| 640×640| 16.5G|7.23M|14.0| 56.0|37.2 012| YOLOv5-Lite<sub>g</sub><sup>ours</sup>| 640×640|15.6G |5.39M|10.9| 57.6|39.1| See the wiki: https://github.com/ppogg/YOLOv5-Lite/wiki/Test-the-map-of-models-about-coco ## Comparison on different platforms Equipment|Computing backend|System|Input|Framework|v5lite-e|v5lite-s|v5lite-c|v5lite-g|YOLOv5s :---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---: Inter|@i5-10210U|window(x86)|640×640|openvino|-|-|46ms|-|131ms Nvidia|@RTX 2080Ti|Linux(x86)|640×640|torch|-|-|-|15ms|14ms Redmi K30|@Snapdragon 730G|Android(armv8)|320×320|ncnn|27ms|38ms|-|-|163ms Xiaomi 10|@Snapdragon 865|Android(armv8)|320×320|ncnn|10ms|14ms|-|-|163ms Raspberrypi 4B|@ARM Cortex-A72|Linux(arm64)|320×320|ncnn|-|84ms|-|-|371ms Raspberrypi 4B|@ARM Cortex-A72|Linux(arm64)|320×320|mnn|-|71ms|-|-|356ms AXera-Pi|Cortex A7@CPU<br />3.6TOPs @NPU|Linux(arm64)|640×640|axpi|-|-|-|22ms|22ms ### The tutorial of 15FPS on Raspberry Pi 4B: [https://zhuanlan.zhihu.com/p/672633849](https://zhuanlan.zhihu.com/p/672633849) * The above is a 4-thread test benchmark * Raspberrypi 4B enable bf16s optimization，[Raspberrypi 64 Bit OS](http://downloads.raspberrypi.org/raspios_arm64/images/raspios_arm64-2020-08-24/) ### qq交流群：993965802 入群答案:剪枝 or 蒸馏 or 量化 or 低秩分解（任意其一均可） ## ·Model Zoo· #### @v5lite-e: Model|Size|Backbone|Head|Framework|Design for :---:|:---:|:---:|:---:|:---:|:--- v5Lite-e.pt|1.7m|shufflenetv2（Megvii）|v5Litee-head|Pytorch|Arm-cpu v5Lite-e.bin<br />v5Lite-e.param|1.7m|shufflenetv2|v5Litee-head|ncnn|Arm-cpu v5Lite-e-int8.bin<br />v5Lite-e-int8.param|0.9m|shufflenetv2|v5Litee-head|ncnn|Arm-cpu v5Lite-e-fp32.mnn|3.0m|shufflenetv2|v5Litee-head|mnn|Arm-cpu v5Lite-e-fp32.tnnmodel<br />v5Lite-e-fp32.tnnproto|2.9m|shufflenetv2|v5Litee-head|tnn|arm-cpu v5Lite-e-320.onnx|3.1m|shufflenetv2|v5Litee-head|onnxruntime|x86-cpu #### @v5lite-s: Model|Size|Backbone|Head|Framework|Design for :---:|:---:|:---:|:---:|:---:|:--- v5Lite-s.pt|3.4m|shufflenetv2（Megvii）|v5Lites-head|Pytorch|Arm-cpu v5Lite-s.bin<br />v5Lite-s.param|3.3m|shufflenetv2|v5Lites-head|ncnn|Arm-cpu v5Lite-s-int8.bin<br />v5Lite-s-int8.param|1.7m|shufflenetv2|v5Lites-head|ncnn|Arm-cpu v5Lite-s.mnn|3.3m|shufflenetv2|v5Lites-head|mnn|Arm-cpu v5Lite-s-int4.mnn|987k|shufflenetv2|v5Lites-head|mnn|Arm-cpu v5Lite-s-fp16.bin<br />v5Lite-s-fp16.xml|3.4m|shufflenetv2|v5Lites-head|openvivo|x86-cpu v5Lite-s-fp3
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8a2f50609cc7f5d6, topic:tensorrt, topic:onnxruntime
matched fp:8a2f50609cc7f5d6, topic:pytorch
matched fp:8a2f50609cc7f5d6, topic:transformer