python3 -m venv <folder>
source bin/activate
- OIDv4_ToolKit is a tool that downloads data sets from open images based on classes
- setting up a seperate virtual environment for "OIDv4" is recommended
git clone https://github.com/EscVM/OIDv4_ToolKit.git
python3 main.py downloader --classes Person --type_csv train --limit 1000
python3 main.py downloader --classes Person --type_csv test --limit 300
- make sure to download the csv annotations with the dataset
.
├── csv_folder
└── Dataset
├── test
│ ├── images
│ └── labels
└── train
├── images
└── labels
- copy the downloaded csv annotations to the csv_folder
- copy both downloaded training and testing images to train/images and test/images
- copy both downloaded labels to /train/labels and /test/labels
- run convert.py to covert the labels into YOLO format
- download dependencys
pip install -r requirements.txt
- train
python3 train.py - test
python3 test.py
python3 predict.py