YOLOv2 in PyTorch

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.

How to run the notebook?

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.

You should have Anaconda installed on your machine:
https://conda.io/docs/user-guide/install/index.html

Download environment.yml file by running this command:

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

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