src/python-doctr/references/classification/README.md

Character classification

The sample training scripts was made to train a character classification model or a orientation classifier with docTR.

Setup

First, you need to install doctr (with pip, for instance)

pip install -e . --upgrade
pip install -r references/requirements.txt

Usage character classification

You can start your training in PyTorch:

python references/classification/train_character.py mobilenet_v3_large --epochs 5 --device 0

Usage orientation classification

You can start your training in PyTorch:

python references/classification/train_orientation.py resnet18 --type page --train_path path/to/your/train_set --val_path path/to/your/val_set --epochs 5

The type can be either page for document images or crop for word crops.

Data format

You need to provide both train_path and val_path arguments to start training. Each path must lead to a folder where the images are stored. For example:

 images
    ├── sample_img_01.png
    ├── sample_img_02.png
    ├── sample_img_03.png
    └── ...

Slack Logging with tqdm

To enable Slack logging using tqdm, you need to set the following environment variables:

• TQDM_SLACK_TOKEN: the Slack Bot Token
• TQDM_SLACK_CHANNEL: you can retrieve it using Right Click on Channel > Copy > Copy link. You should get something like https://xxxxxx.slack.com/archives/yyyyyyyy. Keep only the yyyyyyyy part.

You can follow this page on how to create a Slack App.

Advanced options

Feel free to inspect the multiple script option to customize your training to your own needs!

Character classification:

python references/classification/train_character.py --help

Orientation classification:

python references/classification/train_orientation.py --help