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.
- 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"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/