2022年7月14日:Optimize loss, adopt IOU aware based on smooth L1, and the AP is significantly increased by 0.7

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

• Test platform Radxa Rock3A RK3568 ARM Cortex-A55 CPU,Based on NCNN
• CPU lock frequency 2.0GHz

• Anchor-Free
• Single scale detector head
• Cross grid multiple candidate targets
• Dynamic positive and negative sample allocation

• PiP(Note pytorch CUDA version selection)

  pip install -r requirements.txt
  

• Picture test

  python3 test.py --yaml configs/coco.yaml --weight weights/weight_AP05:0.253207_280-epoch.pth --img data/3.jpg
  

• 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
  

• 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
  

• Perform training tasks

  python3 train.py --yaml configs/coco.yaml
  

• 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
  

• 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
  

• 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
  

• Need to compile ncnn and opencv in advance and modify the path in build.sh

  cd example/ncnn/
  sh build.sh
  ./FastestDet
  

• 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
  

• 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}
  }
  

• https://github.com/Tencent/ncnn