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deep-text-recognition-benchmark
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Update README.md

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Baek JeongHun 2020-02-25 18:13:58 +09:00 committed by GitHub
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@ -22,7 +22,7 @@ The difference between our paper and ICDAR challenge is summarized [here](https:
## Getting Started
### Dependency
- This work was tested with PyTorch 1.1.0, CUDA 9.0, python 3.6 and Ubuntu 16.04. <br> You may need `pip3 install torch==1.1.0`
- This work was tested with PyTorch 1.3.1, CUDA 9.0, python 3.6 and Ubuntu 16.04. <br> You may need `pip3 install torch==1.3.1`. In the paper, expriments were performed with **PyTorch 0.4.1**.
- requirements : lmdb, pillow, torchvision, nltk, natsort
```
pip3 install lmdb pillow torchvision nltk natsort
@ -69,13 +69,14 @@ CUDA_VISIBLE_DEVICES=0 python3 train.py \
--select_data MJ-ST --batch_ratio 0.5-0.5 \
--Transformation None --FeatureExtraction VGG --SequenceModeling BiLSTM --Prediction CTC
```
2. Test CRNN[10] model
2. Test CRNN[10] model. If you want to evaluate IC15-2077, check [data filtering part](https://github.com/clovaai/deep-text-recognition-benchmark/blob/c27abe6b4c681e2ee0784ad966602c056a0dd3b5/dataset.py#L148).
```
CUDA_VISIBLE_DEVICES=0 python3 test.py \
--eval_data data_lmdb_release/evaluation --benchmark_all_eval \
--Transformation None --FeatureExtraction VGG --SequenceModeling BiLSTM --Prediction CTC \
--saved_model saved_models/None-VGG-BiLSTM-CTC-Seed1111/best_accuracy.pth
```
3. Try to train and test our best accuracy combination (TPS-ResNet-BiLSTM-Attn) also. ([download pretrained model](https://drive.google.com/drive/folders/15WPsuPJDCzhp2SvYZLRj8mAlT3zmoAMW))
```
CUDA_VISIBLE_DEVICES=0 python3 train.py \