106 lines
4.3 KiB
Markdown
106 lines
4.3 KiB
Markdown
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# CRAFT-train
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On the official CRAFT github, there are many people who want to train CRAFT models.
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However, the training code is not published in the official CRAFT repository.
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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)
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The trained model with this code recorded a level of performance similar to that of the original paper.
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```bash
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├── config
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│ ├── syn_train.yaml
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│ └── custom_data_train.yaml
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├── data
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│ ├── pseudo_label
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│ │ ├── make_charbox.py
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│ │ └── watershed.py
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│ ├── boxEnlarge.py
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│ ├── dataset.py
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│ ├── gaussian.py
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│ ├── imgaug.py
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│ └── imgproc.py
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├── loss
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│ └── mseloss.py
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├── metrics
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│ └── eval_det_iou.py
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├── model
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│ ├── craft.py
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│ └── vgg16_bn.py
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├── utils
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│ ├── craft_utils.py
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│ ├── inference_boxes.py
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│ └── utils.py
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├── trainSynth.py
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├── train.py
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├── train_distributed.py
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├── eval.py
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├── data_root_dir (place dataset folder here)
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└── exp (model and experiment result files will saved here)
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```
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### Installation
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Install using `pip`
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``` bash
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pip install -r requirements.txt
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```
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### Training
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1. Put your training, test data in the following format
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```
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└── data_root_dir (you can change root dir in yaml file)
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├── ch4_training_images
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│ ├── img_1.jpg
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│ └── img_2.jpg
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├── ch4_training_localization_transcription_gt
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│ ├── gt_img_1.txt
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│ └── gt_img_2.txt
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├── ch4_test_images
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│ ├── img_1.jpg
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│ └── img_2.jpg
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└── ch4_training_localization_transcription_gt
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├── gt_img_1.txt
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└── gt_img_2.txt
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```
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* localization_transcription_gt files format :
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```
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377,117,463,117,465,130,378,130,Genaxis Theatre
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493,115,519,115,519,131,493,131,[06]
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374,155,409,155,409,170,374,170,###
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```
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2. Write configuration in yaml format (example config files are provided in `config` folder.)
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* To speed up training time with multi-gpu, set num_worker > 0
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3. Put the yaml file in the config folder
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4. Run training script like below (If you have multi-gpu, run train_distributed.py)
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5. Then, experiment results will be saved to ```./exp/[yaml]``` by default.
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* Step 1 : To train CRAFT with SynthText dataset from scratch
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* Note : This step is not necessary if you use <a href="https://drive.google.com/file/d/1enVIsgNvBf3YiRsVkxodspOn55PIK-LJ/view?usp=sharing">this pretrain</a> as a checkpoint when start training step 2. You can download and put it in `exp/CRAFT_clr_amp_29500.pth` and change `ckpt_path` in the config file according to your local setup.
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```
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CUDA_VISIBLE_DEVICES=0 python3 trainSynth.py --yaml=syn_train
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```
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* Step 2 : To train CRAFT with [SynthText + IC15] or custom dataset
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```
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CUDA_VISIBLE_DEVICES=0 python3 train.py --yaml=custom_data_train ## if you run on single GPU
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CUDA_VISIBLE_DEVICES=0,1 python3 train_distributed.py --yaml=custom_data_train ## if you run on multi GPU
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```
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### Arguments
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* ```--yaml``` : configuration file name
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### Evaluation
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* In the official repository issues, the author mentioned that the first row setting F1-score is around 0.75.
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* In the official paper, it is stated that the result F1-score of the second row setting is 0.87.
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* If you adjust post-process parameter 'text_threshold' from 0.85 to 0.75, then F1-score reaches to 0.856.
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* It took 14h to train weak-supervision 25k iteration with 8 RTX 3090 Ti.
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* Half of GPU assigned for training, and half of GPU assigned for supervision setting.
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| Training Dataset | Evaluation Dataset | Precision | Recall | F1-score | pretrained model |
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| ------------- |-----|:-----:|:-----:|:-----:|-----:|
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| SynthText | ICDAR2013 | 0.801 | 0.748 | 0.773| <a href="https://drive.google.com/file/d/1enVIsgNvBf3YiRsVkxodspOn55PIK-LJ/view?usp=sharing">download link</a>|
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| SynthText + ICDAR2015 | ICDAR2015 | 0.909 | 0.794 | 0.848| <a href="https://drive.google.com/file/d/1qUeZIDSFCOuGS9yo8o0fi-zYHLEW6lBP/view">download link</a>|
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