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:zap: A newly designed ultra lightweight anchor free target detection algorithm， weight only 250K parameters， reduces the time consumption by 10% compared with yolo-fastest, and the post-processing is simpler
| Date | Stars |
|---|---|
| 2026-07-24 | 855 |
| 2026-07-25 | 855 |
| 2026-07-28 | 856 |
| 2026-07-30 | 856 |
| 2026-08-06 | 856 |
Today
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Momentum
0.0
growth rate 0.00%/day
***2022.7.14:Optimize loss, adopt IOU aware based on smooth L1, and the AP is significantly increased by 0.7***
# :zap:FastestDet:zap:
[](https://zenodo.org/badge/latestdoi/508635170)
* ***Faster! Stronger! Simpler!***
* ***It has better performance and simpler feature map post-processing than Yolo-fastest***
* ***The performance is 10% higher than Yolo-fastest***
* ***The coco evaluation index increased by 1.2% compared with the map0.5 of Yolo-fastestv2***
* ***算法介绍：https://zhuanlan.zhihu.com/p/536500269 交流qq群:1062122604***
# Evaluating indicator/Benchmark
Network|mAPval 0.5|mAPval 0.5:0.95|Resolution|Run Time(4xCore)|Run Time(1xCore)|Params(M)
:---:|:---:|:---:|:---:|:---:|:---:|:---:
[yolov5s](https://github.com/ultralytics/yolov5)|56.8%|37.4%|640X640|395.31ms|1139.16ms|7.2M
[yolov6n](https://github.com/meituan/YOLOv6)|-|30.8%|416X416|109.24ms|445.44ms|4.3M
[yolox-nano](https://github.com/Megvii-BaseDetection/YOLOX)|-|25.8%|416X416|76.31ms|191.16ms|0.91M
[nanodet_m](https://github.com/RangiLyu/nanodet)|-|20.6%|320X320|49.24ms|160.35ms|0.95M
[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
[yolo-fastestv2](https://github.com/dog-qiuqiu/Yolo-FastestV2/tree/main/modelzoo)|24.10%|-|352X352|23.8ms|68.9ms|0.25M
FastestDet|25.3%|13.0%|352X352|23.51ms|70.62ms|0.24M
* ***Test platform Radxa Rock3A RK3568 ARM Cortex-A55 CPU，Based on [NCNN](https://github.com/Tencent/ncnn)***
* ***CPU lock frequency 2.0GHz***
# Improvement
* Anchor-Free
* Single scale detector head
* Cross grid multiple candidate targets
* Dynamic positive and negative sample allocation
# Multi-platform benchmark
Equipment|Computing backend|System|Framework|Run time(Single core)|Run time(Multi core)
:---:|:---:|:---:|:---:|:---:|:---:
Radxa rock3a|RK3568(arm-cpu)|Linux(aarch64)|ncnn|70.62ms|23.51ms
Radxa rock3a|RK3568(NPU)|Linux(aarch64)|rknn|28ms|-
Qualcomm|Snapdragon 835(arm-cpu)|Android(aarch64)|ncnn|32.34ms|16.24ms
Intel|i7-8700(X86-cpu)|Linux(amd64)|ncnn|4.51ms|4.33ms
# How to use
## Dependent installation
* PiP(Note pytorch CUDA version selection)
```
pip install -r requirements.txt
```
## Test
* Picture test
```
python3 test.py --yaml configs/coco.yaml --weight weights/weight_AP05:0.253207_280-epoch.pth --img data/3.jpg
```
<div align=center>
<img src="https://github.com/dog-qiuqiu/FastestDet/blob/main/result.png"> />
</div>
## How to train
### Building data sets(The dataset is constructed in the same way as darknet yolo)
* The format of the data set is the same as that of Darknet Yolo, Each image corresponds to a .txt label file. The label format is also based on Darknet Yolo's data set label format: "category cx cy wh", where category is the category subscript, cx, cy are the coordinates of the center point of the normalized label box, and w, h are the normalized label box The width and height, .txt label file content example as follows:
```
11 0.344192634561 0.611 0.416430594901 0.262
14 0.509915014164 0.51 0.974504249292 0.972
```
* The image and its corresponding label file have the same name and are stored in the same directory. The data file structure is as follows:
```
.
├── train
│ ├── 000001.jpg
│ ├── 000001.txt
│ ├── 000002.jpg
│ ├── 000002.txt
│ ├── 000003.jpg
│ └── 000003.txt
└── val
├── 000043.jpg
├── 000043.txt
├── 000057.jpg
├── 000057.txt
├── 000070.jpg
└── 000070.txt
```
* Generate a dataset path .txt file, the example content is as follows：
train.txt
```
/home/qiuqiu/Desktop/dataset/train/000001.jpg
/home/qiuqiu/Desktop/dataset/train/000002.jpg
/home/qiuqiu/Desktop/dataset/train/00Excerpt of 8,751 characters
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26
China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7138c38c3cc656ff, topic:computer-vision, topic:object-detection
matched fp:7138c38c3cc656ff, topic:deep-learning