https://github.com/wovchena/text-detection-fots.pytorch
FOTS text detection branch reimplementation, hmean: 83.3%
https://github.com/wovchena/text-detection-fots.pytorch
cvpr2018 fots pytorch text-detection
Last synced: 5 months ago
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FOTS text detection branch reimplementation, hmean: 83.3%
- Host: GitHub
- URL: https://github.com/wovchena/text-detection-fots.pytorch
- Owner: Wovchena
- Created: 2019-01-29T13:05:03.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-01-09T10:26:13.000Z (over 5 years ago)
- Last Synced: 2025-03-30T13:51:16.281Z (6 months ago)
- Topics: cvpr2018, fots, pytorch, text-detection
- Language: Python
- Homepage:
- Size: 85.9 KB
- Stars: 79
- Watchers: 5
- Forks: 21
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
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README
# [FOTS: Fast Oriented Text Spotting with a Unified Network](https://arxiv.org/abs/1801.01671) text detection branch reimplementation ([PyTorch](https://pytorch.org/))
## Train
1. Train with SynthText for 9 epochs
```sh
time python3 train.py --train-folder SynthText/ --batch-size 21 --batches-before-train 2
```
At this point the result was `Epoch 8: 100%|█████████████| 390/390 [08:28<00:00, 1.00it/s, Mean loss=0.98050]`.
2. Train with ICDAR15
Replace a data set in `data_set = datasets.SynthText(args.train_folder, datasets.transform)` with `datasets.ICDAR2015` in [`train.py`](./train.py) and run
```sh
time python3 train.py --train-folder icdar15/ --continue-training --batch-size 21 --batches-before-train 2
```
It is expected that the provided `--train-folder` contains unzipped `ch4_training_images` and `ch4_training_localization_transcription_gt`. To avoid saving model at each epoch, the line `if True:` in [`train.py`](./train.py) can be replaced with `if epoch > 60 and epoch % 6 == 0:`
The result was `Epoch 582: 100%|█████████████| 48/48 [01:05<00:00, 1.04s/it, Mean loss=0.11290]`.
### Learning rate schedule:
Epoch 175: reducing learning rate of group 0 to 5.0000e-04.
Epoch 264: reducing learning rate of group 0 to 2.5000e-04.
Epoch 347: reducing learning rate of group 0 to 1.2500e-04.
Epoch 412: reducing learning rate of group 0 to 6.2500e-05.
Epoch 469: reducing learning rate of group 0 to 3.1250e-05.
Epoch 525: reducing learning rate of group 0 to 1.5625e-05.
Epoch 581: reducing learning rate of group 0 to 7.8125e-06.
## Test
```sh
python3 test.py --images-folder ch4_test_images/ --output-folder res/ --checkpoint epoch_582_checkpoint.pt && zip -jmq runs/u.zip res/* && python2 script.py -g=gt.zip -s=runs/u.zip
```
`ch4_training_images` and `ch4_training_localization_transcription_gt` are available in [Task 4.4: End to End (2015 edition)](http://rrc.cvc.uab.es/?ch=4&com=downloads). `script.py` and `ch4_test_images` can be found in [My Methods](https://rrc.cvc.uab.es/?ch=4&com=mymethods&task=1) (`Script: IoU` and `test set samples`).
It gives `Calculated!{"precision": 0.8694968553459119, "recall": 0.7987481945113144, "hmean": 0.8326223337515684, "AP": 0}`.
The pretrained models are here: https://drive.google.com/open?id=1xaVshLRrMEkb9LA46IJAZhlapQr3vyY2
[`test.py`](./test.py) has a commented code to visualize results.
## Difference with the paper
1. The model is different compared to what the paper describes. An explanation is in [`model.py`](./model.py).
2. The authors of FOTS could not train on clipped words because they also have a recognition branch. The whole word is required to be present on an image to be able to be recognized correctly. This reimplementation has only detection branch and that allows to train on crops of the words.
3. The paper suggest using some other data sets in addition. Training on SynthText is simplified in this reimplementation.