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https://github.com/dog-qiuqiu/FastestDet
: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
https://github.com/dog-qiuqiu/FastestDet
computer-vision deep-learning object-detection
Last synced: 28 days ago
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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
- Host: GitHub
- URL: https://github.com/dog-qiuqiu/FastestDet
- Owner: dog-qiuqiu
- License: bsd-3-clause
- Created: 2022-06-29T09:55:30.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-03-16T15:19:16.000Z (over 1 year ago)
- Last Synced: 2024-02-17T18:36:56.659Z (4 months ago)
- Topics: computer-vision, deep-learning, object-detection
- Language: Python
- Homepage:
- Size: 24.4 MB
- Stars: 707
- Watchers: 12
- Forks: 126
- Open Issues: 30
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Lists
- awesome-stars - dog-qiuqiu/FastestDet - fastest, and the post-processing is simpler (Python)
- awesome-stars - dog-qiuqiu/FastestDet - :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 (Python)
- awesome-anchor-free-object-detection - dog-qiuqiu/FastestDet - qiuqiu/FastestDet?style=social"/> : ⚡ 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. (**[知乎 2022](https://zhuanlan.zhihu.com/p/536500269)**) (Frameworks)
- awesome-yolo-object-detection - dog-qiuqiu/FastestDet - qiuqiu/FastestDet?style=social"/> : ⚡ 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. "知乎「马雪浩」《[FastestDet: 比yolo-fastest更快！更强！更简单！全新设计的超实时Anchor-free目标检测算法](https://zhuanlan.zhihu.com/p/536500269)》"。 "微信公众号「计算机视觉研究院」《[FastestDet：比yolov5更快！更强！全新设计的超实时Anchor-free目标检测算法（附源代码下载）](https://mp.weixin.qq.com/s/Bskc5WQd8ujy16Jl4qekjQ)》"。 (Lighter and Deployment Frameworks)
README
***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
```
/>
## 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/000003.jpg
```
val.txt
```
/home/qiuqiu/Desktop/dataset/val/000070.jpg
/home/qiuqiu/Desktop/dataset/val/000043.jpg
/home/qiuqiu/Desktop/dataset/val/000057.jpg
```
* Generate the .names category label file, the sample content is as follows:
category.names
```
person
bicycle
car
motorbike
...
```
* The directory structure of the finally constructed training data set is as follows:
```
.
├── category.names # .names category label file
├── train # train dataset
│ ├── 000001.jpg
│ ├── 000001.txt
│ ├── 000002.jpg
│ ├── 000002.txt
│ ├── 000003.jpg
│ └── 000003.txt
├── train.txt # train dataset path .txt file
├── val # val dataset
│ ├── 000043.jpg
│ ├── 000043.txt
│ ├── 000057.jpg
│ ├── 000057.txt
│ ├── 000070.jpg
│ └── 000070.txt
└── val.txt # val dataset path .txt file
```
### Build the training .yaml configuration file
* Reference./configs/coco.yaml
```
DATASET:
TRAIN: "/home/qiuqiu/Desktop/coco2017/train2017.txt" # Train dataset path .txt file
VAL: "/home/qiuqiu/Desktop/coco2017/val2017.txt" # Val dataset path .txt file
NAMES: "dataset/coco128/coco.names" # .names category label file
MODEL:
NC: 80 # Number of detection categories
INPUT_WIDTH: 352 # The width of the model input image
INPUT_HEIGHT: 352 # The height of the model input image
TRAIN:
LR: 0.001 # Train learn rate
THRESH: 0.25 # ？？？？
WARMUP: true # Trun on warm up
BATCH_SIZE: 64 # Batch size
END_EPOCH: 350 # Train epichs
MILESTIONES: # Declining learning rate steps
- 150
- 250
- 300
```
### Train
* Perform training tasks
```
python3 train.py --yaml configs/coco.yaml
```
### Evaluation
* Calculate map evaluation
```
python3 eval.py --yaml configs/coco.yaml --weight weights/weight_AP05:0.253207_280-epoch.pth
```
* COCO2017 evaluation
```
creating index...
index created!
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=30.85s).
Accumulating evaluation results...
DONE (t=4.97s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.130
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.253
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.119
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.021
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.129
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.237
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.142
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.208
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.214
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.043
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.236
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.372
```
# Deploy
## Export onnx
* You can export .onnx by adding the --onnx option when executing test.py
```
python3 test.py --yaml configs/coco.yaml --weight weights/weight_AP05:0.253207_280-epoch.pth --img data/3.jpg --onnx
```
## Export torchscript
* You can export .pt by adding the --torchscript option when executing test.py
```
python3 test.py --yaml configs/coco.yaml --weight weights/weight_AP05:0.253207_280-epoch.pth --img data/3.jpg --torchscript
```
## NCNN
* Need to compile ncnn and opencv in advance and modify the path in build.sh
```
cd example/ncnn/
sh build.sh
./FastestDet
```
## onnx-runtime
* You can learn about the pre and post-processing methods of FastestDet in this Sample
```
cd example/onnx-runtime
pip install onnx-runtime
python3 runtime.py
```
# Citation
* If you find this project useful in your research, please consider cite:
```
@misc{=FastestDet,
title={FastestDet: Ultra lightweight anchor-free real-time object detection algorithm.},
author={xuehao.ma},
howpublished = {\url{https://github.com/dog-qiuqiu/FastestDet}},
year={2022}
}
```
# Reference
* https://github.com/Tencent/ncnn