Megvii-CSG / MegReader

A research project for text detection and recognition using PyTorch 1.2.

MegReader

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.

Highlights

• 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.

Install

Requirements

pip install -r requirements.txt

• Python3.7
• PyTorch 1.2 and CUDA 10.0.
• gcc 5.5(Important for compiling)

Compile cuda ops (If needed)

cd PATH_TO_OPS

python setup.py build_ext --inplace

ops may be used:

• DeformableConvV2 assets/ops/dcn
• CTC2DLoss ops/ctc_2d

Configuration(optional)

Edit configurations in config.py.

Training

See detailed options: python3 train.py --help

Datasets

We provide data loading implementation with annotation packed with json for quick start. Also, lmdb format data are now available too. You can refer the usage in demo. Datasets used in our recognition experiments can be downloaded from onedrive. The transform script are provide to convert json format data to lmdb.

Non-distributed

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

Distributed(recommended for multi-gpu training)

python3 -m torch.distributed.launch --nproc_per_node=NUM_GPUS train.py PATH_TO_EXPERIMENT.yaml -d --validate

Evaluating

See detailed options: python3 eval.py --help.

Keeping ratio tesing is recommended: python3 eval.py PATH_TO_EXPERIMENT.yaml --resize_mode keep_ratio

Model zoo

Trained models are comming soon.

Progress

Recognition Methods

• 2D CTC
• CRNN
• Attention Decoder
• Rectification

Detection Methods

• Text Snake
• EAST

End-to-end

• Mask Text Spotter

Contributing

Contributing.md