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.

Download VOC 2007 trainval from here.
- Or wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar in terminal.
Download VOC 2007 test from here.
- Or wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar in terminal.
Download the VOC 2012 trainval from here.
- Or wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar

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

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/
```

Name		Name	Last commit message	Last commit date
Latest commit History 29 Commits
inference_data		inference_data
models		models
.gitignore		.gitignore
README.md		README.md
_config.yml		_config.yml
check_test_images.py		check_test_images.py
config.py		config.py
dataset.py		dataset.py
inference_image.py		inference_image.py
inference_video.py		inference_video.py
loss.py		loss.py
prepare_data.py		prepare_data.py
prepare_text_labels.py		prepare_text_labels.py
train.py		train.py
training_utils.py		training_utils.py
transforms.py		transforms.py
utils.py		utils.py