Scaled YOLO v4

This repo exaplins how to train Scaled_YOLOv4 model on your custom dataset.

About Scaled YOlO v4

The YOLOv4 object detection neural network based on the CSP approach, scales both up and down and is applicable to small and large networks while maintaining optimal speed and accuracy. We propose a network scaling approach that modifies not only the depth, width, resolution, but also structure of the network. YOLOv4-large model achieves state-of-the-art results: 55.5% AP (73.4% AP50) for the MS COCO dataset at a speed of ~16 FPS on Tesla V100, while with the test time augmentation, YOLOv4-large achieves 56.0% AP (73.3 AP50). To the best of our knowledge, this is currently the highest accuracy on the COCO dataset among any published work. The YOLOv4-tiny model achieves 22.0% AP (42.0% AP50) at a speed of 443 FPS on RTX 2080Ti, while by using TensorRT, batch size = 4 and FP16-precision the YOLOv4-tiny achieves 1774 FPS.

Model Architecture

Comparison with other models

Dependencies

Some of the main requirements are

pytorch
mish-cuda

Roboflow

I'll be using the Roboflow Scaled YOLOv4 repo in this tutorial, and go through the steps of how

1. Prepare your data
2. Convert data format from PASCAl_VOC to YOLO PyTorch format
3. Installing repo
4. Installing/Troublshooting mish-cuda.

Original Colab Notebook

Dataset Preparation

First to train an object detection model you need a dataset annotated in proper format so download publically available datasets from here. I'd recommend starting by downloading already available dataset. There are alot of format options available in Roboflow but for this repo we need YOLO v5 PyTorch as this Scaled YOLO v4 repo is also built on top on YOLOv5 one.

or you can also make you own dataset using labelimg. A full tutorial for that is here The ouput annotation file for label me is .xml format but our yolov4 model can't read that so we need to convert the dataset into proper format

Dataset Format Conversion

After labelling the data put both img files and .xml files in the same dir. and run the voc2yolov5.py file from scripts

In the first few lines of the .py file change following lines accordingly

os.chdir('D:/cw_projects/paprika/paprika_processed/data_final/') # main dir which contains following subdirectories
dirs = ['train', 'test', 'val']

classes = [ "car", "bike","dog","person"] # put your class names

After that make your data dir like following

📦data
 ┣ 📂test
 ┃ ┣ 📂images
 ┃ ┗ 📂labels
 ┣ 📂train
 ┃ ┣ 📂images
 ┃ ┗ 📂labels
 ┣ 📂valid
 ┃ ┣ 📂images
 ┃ ┗ 📂labels
 ┗ 📜data.yaml

the images will have all the .jpg or .png files and labels will have .txt files for each image and inside data.yaml file add following information

train: /path2dir/data/train/images
val: /path2dir/data/valid/images

nc: 4
names: [ "car", "bike","dog","person"]

Installation

Getting started wiht the installation make a new conda env

conda create -n yolo python=3.6.7

activate env and cd to a dir where you will keep all your data and scripts.

Scaled YOLO v4

Now form inside the scirpts dir copy the jupyter notebooks and place in the same dir you choose first.

Now run the notebook setup_ScaledYOLOv4.ipynb sequentelly Place your data inside the dir you choose Now run the Scaled_YOLOv4.ipynd follow steps inside notebook

mish-cuda

while isntalling mish-cuda you might face following problems

1. cudalas_v2.h not found
2. `cudalas_api.h1 not found

So you can solve it as follows

1. first check if you server have this file or not by typing

find /usr/local/ -name cublas_v2.h 
# or depending on which file is missing
find /usr/local/ -name cublas_api.h 

you will see the dir or cuda versions which have these files so e.g. in my case my server has 3 differec cuda version running and mish-cuda needed 10.1 version which was missing file so I got following output

/usr/local/cuda-10.2/targets/x86_64-linux/include/cublas_api.h
/usr/local/cuda-10.0/include/cublas_api.h

so you can add this file to cuda-10.1 dir by either creating a symblic link or simply copying the files. I copied the files as follows

# export PATH=/usr/local/cuda-10.0/bin:$PATH 
# copy files
sudo cp /usr/local/cuda-10.0/include/cublas_api.h /usr/local/cuda-10.1/targets/x86_64-linux/include/
# or crate symblic link
ln -s /usr/include/cublas_v2.h /usr/local/cuda-10.1/targets/x86_64-linux/include/cublas_v2.h

Evaluation

For in depth evaluation you can run the my_inference.py file form the scripts.

⚠⚠ Note

This repo first creates a cache file where it sores the images and labels for faster training so every time you run the model from start its better to remove those cache files first to avoid getting unwanted errors or warnings.

Citation

@article{wang2020scaled,
  title={{Scaled-YOLOv4}: Scaling Cross Stage Partial Network},
  author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
  journal={arXiv preprint arXiv:2011.08036},
  year={2020}
}