YOLOX-Foot-Dist

Lightweight human detection model generated using a high-quality human dataset. I annotated all the data by myself. Extreme resistance to blur and occlusion. In addition, the recognition rate at short, medium, and long distances has been greatly enhanced. The camera's resistance to darkness and halation has been greatly improved.

The use of CD-COCO: Complex Distorted COCO database for Scene-Context-Aware computer vision has also greatly improved resistance to various types of noise.

• Global distortions

  • Noise
  • Contrast
  • Compression
  • Photorealistic Rain
  • Photorealistic Haze
  • Motion-Blur
  • Defocus-Blur
  • Backlight illumination

• Local distortions

  • Motion-Blur
  • Defocus-Blur
  • Backlight illumination

• Highly accurate foot detection results

  

• Demonstration of detection of feet wearing black socks and bare feet in all-black, hard-to-define clothing

  output_x.mp4

1. Dataset

• COCO-Hand (10,064 images, 31,177 labels, All re-annotated manually)
• http://vision.cs.stonybrook.edu/~supreeth/COCO-Hand.zip
• CD-COCO: Complex Distorted COCO database for Scene-Context-Aware computer vision
• I am adding my own enhancement data to COCO-Hand and re-annotating all images. In other words, only COCO images were cited and no annotation data were cited.
• I have no plans to publish my own dataset.

2. Annotation

Halfway compromises are never acceptable.

Class Name	Class ID
Foot	0

3. Test

• Python 3.10

• onnx 1.14.1+

• onnxruntime-gpu v1.16.1 (TensorRT Execution Provider Enabled Binary. See: onnxruntime-gpu v1.16.1 + CUDA 11.8 + TensorRT 8.5.3 build (RTX3070))

• opencv-contrib-python 4.9.0.80

• numpy 1.24.3

• TensorRT 8.5.3-1+cuda11.8

  # Common ############################################
  pip install opencv-contrib-python numpy onnx
  
  # For ONNX ##########################################
  pip uninstall onnxruntime onnxruntime-gpu
  
  pip install onnxruntime
  or
  pip install onnxruntime-gpu

• Demonstration of models with built-in post-processing (Float32/Float16)

  usage:
    demo_yolox_onnx_foot.py \
    [-h] \
    [-m MODEL] \
    (-v VIDEO | -i IMAGES_DIR) \
    [-ep {cpu,cuda,tensorrt}] \
    [-dvw] \
    [-dwk]
  
  options:
    -h, --help
      show this help message and exit
    -m MODEL, --model MODEL
      ONNX/TFLite file path for YOLOX.
    -v VIDEO, --video VIDEO
      Video file path or camera index.
    -i IMAGES_DIR, --images_dir IMAGES_DIR
      jpg, png images folder path.
    -ep {cpu,cuda,tensorrt}, \
        --execution_provider {cpu,cuda,tensorrt}
      Execution provider for ONNXRuntime.
    -dvw, --disable_video_writer
      Disable video writer. Eliminates the file I/O load associated with automatic
      recording to MP4. Devices that use a MicroSD card or similar for main
      storage can speed up overall processing.
    -dwk, --disable_waitKey
      Disable cv2.waitKey(). When you want to process a batch of still images,
      disable key-input wait and process them continuously.
  

• YOLOX-Body-Head-Hand-Face-Dist - Nano

  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.300
  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.623
  Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.255
  Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.234
  Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494
  Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.775
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.117
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.338
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.401
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.348
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.595
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.811
  per class AP:
  | class   | AP     |
  |:--------|:-------|
  | foot    | 30.010 |
  per class AR:
  | class   | AR     |
  |:--------|:-------|
  | foot    | 40.145 |
  

• YOLOX-Body-Head-Hand-Face-Dist - Tiny

  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.354
  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.702
  Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.312
  Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.288
  Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.554
  Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.815
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.130
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.381
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.437
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.384
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.625
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.848
  per class AP:
  | class   | AP     |
  |:--------|:-------|
  | foot    | 35.381 |
  per class AR:
  | class   | AR     |
  |:--------|:-------|
  | foot    | 43.654 |
  

• YOLOX-Body-Head-Hand-Face-Dist - S

  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.430
  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.782
  Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.410
  Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.359
  Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.659
  Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.881
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.148
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.443
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.495
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.438
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.706
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.907
  per class AP:
  | class   | AP     |
  |:--------|:-------|
  | foot    | 43.025 |
  per class AR:
  | class   | AR     |
  |:--------|:-------|
  | foot    | 49.511 |
  

• YOLOX-Body-Head-Hand-Face-Dist - M

  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.456
  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.809
  Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.441
  Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.385
  Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.686
  Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.890
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.154
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.463
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.512
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.455
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.727
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.914
  per class AP:
  | class   | AP     |
  |:--------|:-------|
  | foot    | 45.574 |
  per class AR:
  | class   | AR     |
  |:--------|:-------|
  | foot    | 51.220 |
  

• YOLOX-Body-Head-Hand-Face-Dist - L

  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.488
  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.835
  Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.496
  Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.420
  Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.709
  Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.901
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.160
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.490
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.537
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.481
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.749
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.923
  per class AP:
  | class   | AP     |
  |:--------|:-------|
  | foot    | 48.823 |
  per class AR:
  | class   | AR     |
  |:--------|:-------|
  | foot    | 53.662 |
  

• YOLOX-Body-Head-Hand-Face-Dist - X

  Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.523
  Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.861
  Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.542
  Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.459
  Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.733
  Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.917
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.166
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.520
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.565
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.511
  Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.769
  Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.933
  per class AP:
  | class   | AP     |
  |:--------|:-------|
  | foot    | 52.254 |
  per class AR:
  | class   | AR     |
  |:--------|:-------|
  | foot    | 56.504 |
  

• Post-Process

  Because I add my own post-processing to the end of the model, which can be inferred by TensorRT, CUDA, and CPU, the benchmarked inference speed is the end-to-end processing speed including all pre-processing and post-processing. EfficientNMS in TensorRT is very slow and should be offloaded to the CPU.

  • NMS default parameter

    param	value	note
    max_output_boxes_per_class	20	Maximum number of outputs per class of one type. 20 indicates that the maximum number of people detected is 20, the maximum number of heads detected is 20, and the maximum number of hands detected is 20. The larger the number, the more people can be detected, but the inference speed slows down slightly due to the larger overhead of NMS processing by the CPU. In addition, as the number of elements in the final output tensor increases, the amount of information transferred between hardware increases, resulting in higher transfer costs on the hardware circuit. Therefore, it would be desirable to set the numerical size to the minimum necessary.
    iou_threshold	0.40	A value indicating the percentage of occlusion allowed for multiple bounding boxes of the same class. 0.40 is excluded from the detection results if, for example, two bounding boxes overlap in more than 41% of the area. The smaller the value, the more occlusion is tolerated, but over-detection may increase.
    score_threshold	0.25	Bounding box confidence threshold. Specify in the range of 0.00 to 1.00. The larger the value, the stricter the filtering and the lower the NMS processing load, but in exchange, all but bounding boxes with high confidence values are excluded from detection. This is a parameter that has a very large percentage impact on NMS overhead.

  • Change NMS parameters

    Use PINTO0309/sam4onnx to rewrite the NonMaxSuppression parameter in the ONNX file.

    For example,

    pip install onnxsim==0.4.33 \
    && pip install -U simple-onnx-processing-tools \
    && pip install -U onnx \
    && python -m pip install -U onnx_graphsurgeon \
        --index-url https://pypi.ngc.nvidia.com
    
    ### max_output_boxes_per_class
    ### Example of changing the maximum number of detections per class to 100.
    sam4onnx \
    --op_name main01_nonmaxsuppression11 \
    --input_onnx_file_path yolox_s_body_head_hand_post_0299_0.4983_1x3x256x320.onnx \
    --output_onnx_file_path yolox_s_body_head_hand_post_0299_0.4983_1x3x256x320.onnx \
    --input_constants main01_max_output_boxes_per_class int64 [100]
    
    ### iou_threshold
    ### Example of changing the allowable area of occlusion to 20%.
    sam4onnx \
    --op_name main01_nonmaxsuppression11 \
    --input_onnx_file_path yolox_s_body_head_hand_post_0299_0.4983_1x3x256x320.onnx \
    --output_onnx_file_path yolox_s_body_head_hand_post_0299_0.4983_1x3x256x320.onnx \
    --input_constants main01_iou_threshold float32 [0.20]
    
    ### score_threshold
    ### Example of changing the bounding box score threshold to 15%.
    sam4onnx \
    --op_name main01_nonmaxsuppression11 \
    --input_onnx_file_path yolox_s_body_head_hand_post_0299_0.4983_1x3x256x320.onnx \
    --output_onnx_file_path yolox_s_body_head_hand_post_0299_0.4983_1x3x256x320.onnx \
    --input_constants main01_score_threshold float32 [0.15]

  • Post-processing structure

    PyTorch alone cannot generate this post-processing.

    

• INT8 quantization (TexasInstruments/YOLOX-ti-lite)

  In my experience, YOLOX has a very large accuracy degradation during quantization due to its structure. The reasons for this and the workaround are examined in detail by TexasInstruments. I have summarized the main points below on how to minimize accuracy degradation during quantization through my own practice. I just put into practice what TexasInstruments suggested, but the degrade in accuracy during quantization was extremely small. Note, however, that the results of the Float16 mixed-precision training before quantization are significantly degraded in accuracy due to the change in activation function to ReLU and many other workarounds, as well as the completely different data sets being benchmarked.

  https://github.com/PINTO0309/onnx2tf?tab=readme-ov-file#7-if-the-accuracy-of-the-int8-quantized-model-degrades-significantly

4. Citiation

If this work has contributed in any way to your research or business, I would be happy to be cited in your literature.

@software{YOLOX-Foot-Dist,
  author={Katsuya Hyodo},
  title={Lightweight human detection model generated using a high-quality human dataset (Foot) and Complex Distorted COCO database for Scene-Context-Aware computer vision},
  url={https://github.com/PINTO0309/PINTO_model_zoo/tree/main/444_YOLOX-Foot-Dist},
  year={2024},
  month={2},
  doi={10.5281/zenodo.10229410},
}

5. Cited

I am very grateful for their excellent work.

• COCO-Hand

  https://vision.cs.stonybrook.edu/~supreeth/

  @article{Hand-CNN,
    title={Contextual Attention for Hand Detection in the Wild},
    author={Supreeth Narasimhaswamy and Zhengwei Wei and Yang Wang and Justin Zhang and Minh Hoai},
    booktitle={International Conference on Computer Vision (ICCV)},
    year={2019},
    url={https://arxiv.org/pdf/1904.04882.pdf}
  }

• CD-COCO: Complex Distorted COCO database for Scene-Context-Aware computer vision

  

  @INPROCEEDINGS{10323035,
    author={Beghdadi, Ayman and Beghdadi, Azeddine and Mallem, Malik and Beji, Lotfi and Cheikh, Faouzi Alaya},
    booktitle={2023 11th European Workshop on Visual Information Processing (EUVIP)},
    title={CD-COCO: A Versatile Complex Distorted COCO Database for Scene-Context-Aware Computer Vision},
    year={2023},
    volume={},
    number={},
    pages={1-6},
    doi={10.1109/EUVIP58404.2023.10323035}
  }

• YOLOX

  https://github.com/Megvii-BaseDetection/YOLOX

  @article{yolox2021,
    title={YOLOX: Exceeding YOLO Series in 2021},
    author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
    journal={arXiv preprint arXiv:2107.08430},
    year={2021}
  }

• YOLOX-ti-lite

  https://github.com/TexasInstruments/edgeai-yolox

• yolox-ti-lite_tflite

  https://github.com/motokimura/yolox-ti-lite_tflite

• YOLOX-Colaboratory-Training-Sample

  高橋かずひと https://github.com/Kazuhito00

  https://github.com/Kazuhito00/YOLOX-Colaboratory-Training-Sample

6. License

Apache License Version 2.0