Environment should have with torch>=1.7 (yolov8) for example:
pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.htmlInstall YOLOv8 requirements:
pip install -r yolov8/requirements.txtInstall DocILE library:
pip install docile-benchmarkBefore running the code it is necessary to edit the dataset config file: yolov8/ultralytics/datasets/docile.yaml.
The file contains information about the dataset:
- path:
<path/to/docile/dataset> - cache_location:
<folder/for/yolo/to/save/cache/files>
YOLOv8 config file with parameters is located at: yolov8/ultralytics/yolo/cfg/default.yaml
python yolov8/train.py \
--model_name yolov8x \
--data_path ylov8/ultralytics/datasets/docile.yaml \
--epochs 30 \
--lr0 0.001 \
--batch 8 \
--imgsz 1280 \
--workers 8 \
--optimizer AdamW \
--model yolov8x.pt \
--char_grid_encoder three_digit_0 \
--ch 6 \
--seed 0 \
--hsv_h 0.0 \
--hsv_s 0.0 \
--hsv_v 0.0 \
--scale 0.0 \
--fliplr 0.0 \
--mosaic 0.0 The above code will reproduce KILE results, to reproduce LIR results, epochs should be changed to 50 and seed to 1
python yolov8/predict.py \
--run_path <path/to/yolov8/output/folder> \
--dataset_path <path/to/docile/dataset>pip install easyocrpython yolov8/predict_image.py \
--checkpoint_path <path/to/checkpoint.pt> \
--output_path <path/to/output/folder> \
--data_path <path/to/folder/with/images>