180 lines
7.0 KiB
Markdown
180 lines
7.0 KiB
Markdown
# :zap:FastestDet:zap:
|
||
|
||
* ***Faster! Stronger! Simpler!***
|
||
* ***It has better single core reasoning performance and simpler feature map post-processing than Yolo-fastest***
|
||
* ***In the ARM CPU of RK3568, the single core reasoning performance is 50% higher than Yolo-fastest***
|
||
* ***The coco evaluation index increased by 3.8% compared with the map0.5 of Yolo-fastest***
|
||
* ***算法介绍：https://zhuanlan.zhihu.com/p/536500269 交流qq群:1062122604***
|
||
# Evaluating indicator/Benchmark
|
||
Network|COCO mAP(0.5)|Resolution|Run Time(4xCore)|Run Time(1xCore)|FLOPs(G)|Params(M)
|
||
:---:|:---:|:---:|:---:|:---:|:---:|:---:
|
||
[Yolo-FastestV1.1](https://github.com/dog-qiuqiu/Yolo-Fastest/tree/master/ModelZoo/yolo-fastest-1.1_coco)|24.40 %|320X320|26.60 ms|75.74 ms|0.252|0.35M
|
||
[Yolo-FastestV2](https://github.com/dog-qiuqiu/Yolo-FastestV2/tree/main/modelzoo)|24.10 %|352X352|23.8 ms|68.9 ms|0.212|0.25M
|
||
FastestDet|27.8%|512X512|21.51ms|34.62ms|*|0.25M
|
||
|
||
* ***Test platform RK3568 CPU，Based on [NCNN](https://github.com/Tencent/ncnn)***
|
||
# Improvement
|
||
* Anchor-Free
|
||
* Single scale detector head
|
||
* Cross grid multiple candidate targets
|
||
* Dynamic positive and negative sample allocation
|
||
# How to use
|
||
## Dependent installation
|
||
* PiP(Note pytorch CUDA version selection)
|
||
```
|
||
pip install -r requirements.txt
|
||
```
|
||
## Test
|
||
* Picture test
|
||
```
|
||
python3 test.py --yaml configs/config.yaml --weight weights/weight_AP05\:0.278_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/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/config.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: 512 # The width of the model input image
|
||
INPUT_HEIGHT: 512 # 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/config.yaml
|
||
```
|
||
### Evaluation
|
||
* Calculate map evaluation
|
||
```
|
||
python3 eval.py --yaml configs/config.yaml --weight weights/weight_AP05\:0.278_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 ] = 0.140
|
||
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.278
|
||
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.128
|
||
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.018
|
||
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.103
|
||
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.232
|
||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.157
|
||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.225
|
||
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.231
|
||
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.032
|
||
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.201
|
||
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.359
|
||
|
||
```
|
||
# Deploy
|
||
## NCNN
|
||
* Waiting for update
|
||
# 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
|