/yolov1_pytorch

A simple implementation of YOLOv1 PyTorch and training on the PASCAL VOC dataset.

Primary LanguagePython

YOLOV1 Training Pipeline on the PASCAL VOC Data using the PyTorch Framework

This repository contains YOLOV1 training pipeline on the PASCAL VOC 2007 and 2012 data using the PyTorch framework. A few points:

Note: Use this repository/code for any project/learning. A simple reference/attribution to the repository is enough. Completely OPEN for any use.

Steps to Train

First of All, Download and Extract the Data (Download to any directory of your choice)

Execute the following commands in the directory where the downloaded .tar files are present.

tar xf VOCtrainval_06-Nov-2007.tar

tar xf VOCtest_06-Nov-2007.tar 

tar xf VOCtrainval_11-May-2012.tar

Prepare the Text Files and Labels

  • Then execute prepare_data.py with the correct path argument to the VOCdevkit parent directory. Simply, the next folder after the path in the arugment should be VOCdevkit The following is an example:

    python prepare_data.py --path my_pacal_voc_data

    In the above command my_pacal_voc_data should contain the VOCdevkit directory for the dataset.

  • Then execute prepare_text_labels.py with correct command line argument paths to the VOC 2007 and 2012 Annotations directory. See text_labels.py to know what the exact path should be. Example command:

    python prepare_text_labels.py --annotations-2007 VOCdevkit/VOC2007/Annotations/ --annotations-2012 VOCdevkit/VOC2012/Annotations/

Train

  • Execute python train.py.

References