## TEXT ANGLE CLASSIFICATION
### DATA PREPARATION
Please organize the dataset as follows:
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:
```
ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/cls/dataset
```
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/word_001.jpg 0
train/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
Write the prepared txt file and image folder path into the configuration file under the `Train/Eval.dataset.label_file_list` and `Train/Eval.dataset.data_dir` fields, the absolute path of the image consists of the `Train/Eval.dataset.data_dir` field and the image name recorded in the txt file.
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 --gpus.
# Start training, the following command has been written into the train.sh file, just modify the configuration file path in the file
python3 -m paddle.distributed.launch --gpus '0,1,2,3,4,5,6,7' 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 uncomment the `RecAug` and `RandAugment` fields under `Train.dataset.transforms` 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:
[rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py)
[randaugment.py](../../ppocr/data/imaug/randaugment.py)
- Training
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 1000 iter. The following content will be saved during training:
```bash
├── best_accuracy.pdopt # Optimizer parameters for the best model
├── best_accuracy.pdparams # Parameters of the best model
├── best_accuracy.states # Metric info and epochs of the best model
├── config.yml # Configuration file for this experiment
├── latest.pdopt # Optimizer parameters for the latest model
├── latest.pdparams # Parameters of the latest model
├── latest.states # Metric info and epochs of the latest model
└── train.log # Training log
```
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 dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/cls/cls_mv3.yml` file.
```
export CUDA_VISIBLE_DEVICES=0
# GPU evaluation, Global.checkpoints is the weight to be tested
python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy
```
### PREDICTION
* Training engine prediction
Using the model trained by paddleocr, you can quickly get prediction through the following script.
Use `Global.infer_img` to specify the path of the predicted picture or folder, and use `Global.checkpoints` to specify the weight:
```
# Predict English results
python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words_en/word_10.png
```
Input image:
Get the prediction result of the input image:
```
infer_img: doc/imgs_words_en/word_10.png
result: ('0', 0.9999995)
```