Another PyTorch implementation of YOLOv2 object detection algorithm. I tried to make it a bit cleaner than some other implementations.

• There is a Jupyter notebook that you can use to test your own images or run the pretrained models on your camera.
• I tested this on PyTorch 0.4.1 but it should also work with 0.4.0.
• Training is not implemented. I started working on it but I never got to finish it.

• You need to download pretrained weights in order to run the notebook. You can download them here:
  YOLOv2 608x608 COCO
  Tiny YOLO VOC 2007+2012
• After that, you need to create a folder named weights and put them inside this folder.
• Now you should be able to run it if you have the required packages installed.

1. You should have Anaconda installed on your machine: https://conda.io/docs/user-guide/install/index.html
2. Download environment.yml file by running this command:

wget https://raw.githubusercontent.com/furkanu/yolov2-pytorch/master/environment.yml

3. Then, run the command below to create the conda environment with the required packages installed. The environment will be named "yolov2-pytorch" but you can change it by editing the first line of the environment.yml file.

conda env create -f environment.yml

4. After your environment has been created successfully, you can run these commands to add a kernel that you can select when running the notebook.

#replace "yolov2-pytorch" with your environment name if you changed it.
source activate yolov2-pytorch 
python -m ipykernel install --user --name yolov2-pytorch --display-name "yolov2-pytorch"

This project took inspiration and/or code from these projects and courses/tutorials:

• Fast.ai
• https://github.com/ayooshkathuria/PyTorch-YOLO-v2
• https://github.com/marvis/pytorch-yolo2
• https://pjreddie.com/darknet/yolov2/
• Deeplearning.ai > Convolutional Neural Networks > Week 3 > Car detection for Autonomous Driving
• https://blog.paperspace.com/how-to-implement-a-yolo-object-detector-in-pytorch/