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README.md
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README.md
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！！！！因为ncnn onnx2ncnn的bug导致crop算子计算有问题，故模型的指标需重新测试:https://github.com/Tencent/ncnn/pull/3999
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！！！！因为ncnn onnx2ncnn的bug导致crop算子计算有问题，修复后模型的指标作了调整修正:https://github.com/Tencent/ncnn/pull/3999
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！！！！Because the bug of ncnn onnx2ncnn causes problems in the calculation of the crop operator, the indicators of the model need to be retested :https://github.com/Tencent/ncnn/pull/3999
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# :zap:FastestDet:zap:[](https://zenodo.org/badge/latestdoi/508635170)
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* ***Faster! Stronger! Simpler!***
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* ***It has better single core reasoning performance and simpler feature map post-processing than Yolo-fastest***
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* ***In the ARM CPU of RK3568, the single core reasoning performance is 50% higher than Yolo-fastest***
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* ***The coco evaluation index increased by 3.8% compared with the map0.5 of Yolo-fastest***
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* ***It has better performance and simpler feature map post-processing than Yolo-fastest***
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* ***The performance is 10% higher than Yolo-fastest***
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* ***The coco evaluation index increased by 0.9% compared with the map0.5 of Yolo-fastestv2***
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* ***算法介绍：https://zhuanlan.zhihu.com/p/536500269 交流qq群:1062122604***
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# Evaluating indicator/Benchmark
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Network|mAPval 0.5|mAPval 0.5:0.95|Resolution|Run Time(4xCore)|Run Time(1xCore)|Params(M)
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@ -17,7 +16,7 @@ Network|mAPval 0.5|mAPval 0.5:0.95|Resolution|Run Time(4xCore)|Run Time(1xCore)|
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[nanodet_m](https://github.com/RangiLyu/nanodet)|-|20.6%|320X320|49.24ms|160.35ms|0.95M
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[yolo-fastestv1.1](https://github.com/dog-qiuqiu/Yolo-Fastest/tree/master/ModelZoo/yolo-fastest-1.1_coco)|24.40%|-|320X320|26.60ms|75.74ms|0.35M
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[yolo-fastestv2](https://github.com/dog-qiuqiu/Yolo-FastestV2/tree/main/modelzoo)|24.10%|-|352X352|23.8ms|68.9ms|0.25M
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FastestDet|27.8%|14.0%|512X512|21.51ms|34.62ms|0.25M
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FastestDet|25.0%|12.3%|352X352|23.51ms|70.62ms|0.24M
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* ***Test platform Radxa Rock3A RK3568 ARM Cortex-A55 CPU，Based on [NCNN](https://github.com/Tencent/ncnn)***
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* ***CPU lock frequency 2.0GHz***
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# Improvement
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# Multi-platform benchmark
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Equipment|Computing backend|System|Framework|Run time(Single core)|Run time(Multi core)
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:---:|:---:|:---:|:---:|:---:|:---:
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Radxa rock3a|RK3568(arm-cpu)|Linux(aarch64)|ncnn|34.62ms|21.51ms
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Qualcomm|Snapdragon 835(arm-cpu)|Android(aarch64)|ncnn|28.34ms|16.24ms
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AMD|R5-5600(X86-cpu)|Linux(amd64)|ncnn|2.16ms|1.73ms
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Intel|i7-8700(X86-cpu)|Linux(amd64)|ncnn|5.21ms|4.73ms
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Radxa rock3a|RK3568(arm-cpu)|Linux(aarch64)|ncnn|70.62ms|23.51ms
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Qualcomm|Snapdragon 835(arm-cpu)|Android(aarch64)|ncnn|32.34ms|16.24ms
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Intel|i7-8700(X86-cpu)|Linux(amd64)|ncnn|4.51ms|4.33ms
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# How to use
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## Dependent installation
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* PiP(Note pytorch CUDA version selection)
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## Test
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* Picture test
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```
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python3 test.py --yaml configs/coco.yaml --weight weights/weight_AP05\:0.278_280-epoch.pth --img data/3.jpg
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python3 test.py --yaml configs/coco.yaml --weight weights/coco_ap05_0.250_280epoch.pth --img data/3.jpg
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```
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<div align=center>
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<img src="https://github.com/dog-qiuqiu/FastestDet/blob/main/result.png"> />
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@ -128,8 +126,8 @@ DATASET:
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NAMES: "dataset/coco128/coco.names" # .names category label file
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MODEL:
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NC: 80 # Number of detection categories
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INPUT_WIDTH: 512 # The width of the model input image
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INPUT_HEIGHT: 512 # The height of the model input image
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