A project for research in text detection and recognition using PyTorch 1.2.
This project is originated from the research repo, which heavily relies on closed-source libraries, of CSG-Algorithm team of Megvii(https://megvii.com). We are in ongoing progress to transfer models into this repo gradually, released implementations are listed in Progress.
- Implementations of representative text detection and recognition methods.
- An effective framework for conducting experiments: We use yaml files to configure experiments, making it convenient to take experiments.
- Thorough logging features which make it easy to follow and analyze experimental results.
- CPU/GPU compatible for training and inference.
- Distributed training support.
pip install -r requirements.txt
- Python3.7
- PyTorch 1.2 and CUDA 10.0.
- gcc 5.5(Important for compiling)
cd PATH_TO_OPS
python setup.py build_ext --inplace
ops may be used:
- DeformableConvV2
assets/ops/dcn - CTC2DLoss
ops/ctc_2d
Edit configurations in config.py.
See detailed options: python3 train.py --help
We provide data loading implementation with annotation packed with json for quick start. Datasets used in our recognition experiments can be downloaded from onedrive.
python3 train.py PATH_TO_EXPERIMENT.yaml --validate --visualize --name NAME_OF_EXPERIMENT
Following we provide some of configurations of the released recognition models:
- CRNN:
experiments/recognition/crnn.yaml - 2D CTC:
experiments/recognition/res50-ppm-2d-ctc.yaml - Attention Decoder:
experiments/recognition/fpn50-attention-decoder.yaml
python3 -m torch.distributed.launch --nproc_per_node=NUM_GPUS train.py PATH_TO_EXPERIMENT.yaml -d --validate
See detailed options: python3 eval.py --help.
Keeping ratio tesing is recommended: python3 eval.py PATH_TO_EXPERIMENT.yaml --resize_mode keep_ratio
Trained models are comming soon.
- 2D CTC
- CRNN
- Attention Decoder
- Rectification
- Text Snake
- EAST
- Mask Text Spotter