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

⚡FastestDet⚡

DOI image image image

  • 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