2022.7.14:Optimize loss, adopt IOU aware based on smooth L1, and the AP is significantly increased by 0.7

:zap:FastestDet:zap:

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	56.8%	37.4%	640X640	395.31ms	1139.16ms	7.2M
yolov6n	-	30.8%	416X416	109.24ms	445.44ms	4.3M
yolox-nano	-	25.8%	416X416	76.31ms	191.16ms	0.91M
nanodet_m	-	20.6%	320X320	49.24ms	160.35ms	0.95M
yolo-fastestv1.1	24.40%	-	320X320	26.60ms	75.74ms	0.35M
yolo-fastestv2	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
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

dog-qiuqiu / FastestDet

readme