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⚡ 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

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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
    
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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

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⚡ 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

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