• Demo
• Dataset
• AP
• mAP
• Classes
• Training Log
• Weights
• How TO Use
• Environment
• Speed
• YouTube Link of Video Demo
• Google Colab
• Tutorial
  • Run your custom object detection
• Citation
• References

YOLOv4	YOLOv3

Conclusion: More significant improvement in low AP classes.

for 10,000 iterations

1. car: with 101314 labels
2. truck: with 6313 labels
3. pedestrian: with 10637 labels
4. bicyclist: with 1442 labels
5. light: with 12700 labels

YOLOv4	YOLOv3

Conclusion: The speed of convergence in YOLOv4 is much faster than that in YOLOv3

• Use with YOLOv4 AlexeyAB

• VM: Google Colaboratory
• GPU: NVIDIA T4 Tensor GPU
• NVIDIA-SMI 470.42.01 Driver Version: 460.32.03 CUDA Version: 11.2
• nvcc: NVIDIA (R) Cuda compiler driver
• Cuda compilation tools, release 11.0, V11.0.221
• Build cuda_11.0_bu.TC445_37.28845127_0

Link

1. verify CUDA version: /usr/local/cuda/bin/nvcc --version
2. check GPU info: nvidia-smi

clone AlexeyAB/darknet repo

git clone https://github.com/AlexeyAB/darknet

change makefile to have GPU and OPENCV enabled

sed -i 's/GPU=0/GPU=1/' Makefile
sed -i 's/CUDNN=0/CUDNN=1/' Makefile
sed -i 's/OPENCV=0/OPENCV=1/' Makefile

Build darknet environment

make

配置config檔

• change line batch to batch=64
• change line subdivisions to subdivisions=16
• change line max_batches to (classes*2000, but not less than number of training images and not less than 6000), f.e. max_batches=6000 if you train for 3 classes
• change line steps to 80% and 90% of max_batches, f.e. steps=4800,5400
• set network size width=416 height=416 or any value multiple of 32:
• change line classes=80 to your number of objects in each of 3 [yolo]-layers
• change [filters=255] to filters=(classes + 5)x3 in the 3 [convolutional] before each [yolo] layer, keep in mind that it only has to be the last [convolutional] before each of the [yolo] layers

準備好以下檔案

1. train.txt
2. test.txt
3. obj.data
4. obj.names
5. pre-trained.weights

./darknet detector train <your_path_of_obj.data> <your_path_of_cfg> <your_path_of_weights> -chart chart.png

• -chart chart.png: 可保存訓練過程

sed -i 's/batch=64/batch=1/' <your_path_of_cfg>
sed -i 's/subdivisions=16/subdivisions=1/' <your_path_of_cfg>

./darknet detector test <your_path_of_obj.data> <your_path_of_cfg> <your_path_of_weights> <your_path_of_input_picture>

./darknet detector map <your_path_of_obj.data> <your_path_of_cfg> <your_path_of_weights>

@misc{bochkovskiy2020yolov4,
      title={YOLOv4: Optimal Speed and Accuracy of Object Detection}, 
      author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao},
      year={2020},
      eprint={2004.10934},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@InProceedings{Wang_2021_CVPR,
    author    = {Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
    title     = {{Scaled-YOLOv4}: Scaling Cross Stage Partial Network},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {13029-13038}
}

• YOLOv4: Optimal Speed and Accuracy of Object Detection: https://arxiv.org/pdf/2004.10934
• Training data from: https://www.kaggle.com/alincijov/self-driving-cars
• Tesing data from: https://youtu.be/z1obnaqPgMA