Requirements

conda install pytorch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1 cudatoolkit=11.3 -c pytorch -c conda-forge
pip install wandb==0.12.9
pip install Shapely==1.8.0

train.sh

CUDA_VISIBLE_DEVICES=$CUDA python train.py --yaml=main

test.sh

read -p "Exp: " dir
rm -rf ./exp/$dir
cd utils/cpp_bindings
sh compile.sh
cd ../..

CUDA_VISIBLE_DEVICES=$CUDA python test.py --yaml=org --dir=$dir
cp ./config/main.yaml ./exp/$dir/

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Referecne: gmuffiness/CRAFT-train

Read more

CRAFT-train

On the official CRAFT github, there are many people who want to train CRAFT models.

However, the training code is not published in the official CRAFT repository.

There are other reproduced codes, but there is a gap between their performance and performance reported in the original paper. (https://arxiv.org/pdf/1904.01941.pdf)

The trained model with this code recorded a level of performance similar to that of the original paper.

├── config
│   ├── syn_train.yaml
│   ├── ic15_train.yaml
├── data
│   ├── pseudo_label
│   │   ├── make_charbox.py
│   │   └── watershed.py
│   ├── boxEnlarge.py
│   ├── dataset.py
│   ├── gaussian.py
│   ├── imgaug.py
│   ├── imgproc.py
├── loss
│   └── mseloss.py
├── metrics
│   └── eval_det_iou.py
├── model
│   ├── craft.py
│   └── vgg16_bn.py
├── utils
│   ├── craft_utils.py
│   ├── inference_boxes.py
│   └── utils.py
├── trainSynth.py
├── trainIC15.py
└── eval.py

Training

1. Write configuration in yaml format
2. Put the yaml file in the config folder
3. Run train code (trainSynth.py or trainIC15.py)
4. Then, experiment results will be saved to ./exp/[yaml] by default.

CUDA_VISIBLE_DEVICES=0,1 python3 trainSynth.py --yaml=syn_train   # run supervision code
CUDA_VISIBLE_DEVICES=0,1 python3 trainIC15.py --yaml=ic15_train   # run weak-supervision code

Arguments

• --yaml : configuration file name

Evaluation

• In the official repository issues, the author mentioned that the first row setting F1-score is around 0.75.
• In the official paper, it is stated that the result F1-score of the second row setting is 0.87.
  • If you adjust post-process parameter 'text_threshold' from 0.85 to 0.75, then F1-score reaches to 0.856.

Training Dataset	Evaluation Dataset	Precision	Recall	F1-score	pretrained model
SynthText	ICDAR2013	0.801	0.748	0.773	download link
SynthText + ICDAR2015	ICDAR2015	0.909	0.794	0.848	download link