The default storage path for training data is `PaddleOCR/train_data/cls`, if you already have a dataset on your disk, just create a soft link to the dataset directory:
please refer to the following to organize your data.
- Training set
First put the training images in the same folder (train_images), and use a txt file (cls_gt_train.txt) to store the image path and label.
* Note: by default, the image path and image label are split with `\t`, if you use other methods to split, it will cause training error
0 and 180 indicate that the angle of the image is 0 degrees and 180 degrees, respectively.
```
" Image file name Image annotation "
train_data/word_001.jpg 0
train_data/word_002.jpg 180
```
The final training set should have the following file structure:
```
|-train_data
|-cls
|- cls_gt_train.txt
|- train
|- word_001.png
|- word_002.jpg
|- word_003.jpg
| ...
```
- Test set
Similar to the training set, the test set also needs to be provided a folder
containing all images (test) and a cls_gt_test.txt. The structure of the test set is as follows:
```
|-train_data
|-cls
|- cls_gt_test.txt
|- test
|- word_001.jpg
|- word_002.jpg
|- word_003.jpg
| ...
```
### TRAINING
PaddleOCR provides training scripts, evaluation scripts, and prediction scripts.
Start training:
```
# Set PYTHONPATH path
export PYTHONPATH=$PYTHONPATH:.
# GPU training Support single card and multi-card training, specify the card number through CUDA_VISIBLE_DEVICES
export CUDA_VISIBLE_DEVICES=0,1,2,3
# Training icdar15 English data
python3 tools/train.py -c configs/cls/cls_mv3.yml
```
- Data Augmentation
PaddleOCR provides a variety of data augmentation methods. If you want to add disturbance during training, please set `distort: true` in the configuration file.
The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse, RandAugment.
Except for RandAugment, each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to:
PaddleOCR supports alternating training and evaluation. You can modify `eval_batch_step` in `configs/cls/cls_mv3.yml` to set the evaluation frequency. By default, it is evaluated every 500 iter and the best acc model is saved under `output/cls_mv3/best_accuracy` during the evaluation process.
If the evaluation set is large, the test will be time-consuming. It is recommended to reduce the number of evaluations, or evaluate after training.
**Note that the configuration file for prediction/evaluation must be consistent with the training.**
### EVALUATION
The evaluation data set can be modified via `configs/cls/cls_reader.yml` setting of `label_file_path` in EvalReader.
```
export CUDA_VISIBLE_DEVICES=0
# GPU evaluation, Global.checkpoints is the weight to be tested
