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135 lines
4.6 KiB
Markdown
135 lines
4.6 KiB
Markdown
## TEXT ANGLE CLASSIFICATION
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### DATA PREPARATION
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Please organize the dataset as follows:
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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:
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```
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ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/cls/dataset
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```
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please refer to the following to organize your data.
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- Training set
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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.
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* 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
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0 and 180 indicate that the angle of the image is 0 degrees and 180 degrees, respectively.
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```
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" Image file name Image annotation "
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train_data/word_001.jpg 0
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train_data/word_002.jpg 180
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```
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The final training set should have the following file structure:
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```
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|-train_data
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|-cls
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|- cls_gt_train.txt
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|- train
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|- word_001.png
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|- word_002.jpg
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|- word_003.jpg
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| ...
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```
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- Test set
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Similar to the training set, the test set also needs to be provided a folder
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containing all images (test) and a cls_gt_test.txt. The structure of the test set is as follows:
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```
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|-train_data
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|-cls
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|- cls_gt_test.txt
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|- test
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|- word_001.jpg
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|- word_002.jpg
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|- word_003.jpg
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| ...
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```
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### TRAINING
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PaddleOCR provides training scripts, evaluation scripts, and prediction scripts.
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Start training:
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```
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# Set PYTHONPATH path
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export PYTHONPATH=$PYTHONPATH:.
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# GPU training Support single card and multi-card training, specify the card number through --gpus. If your paddle version is less than 2.0rc1, please use '--selected_gpus'
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# Start training, the following command has been written into the train.sh file, just modify the configuration file path in the file
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python3 -m paddle.distributed.launch --gpus '0,1,2,3,4,5,6,7' tools/train.py -c configs/cls/cls_mv3.yml
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```
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- Data Augmentation
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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.
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The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse, RandAugment.
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Except for RandAugment, each disturbance method is selected with a 50% probability during the training process. For specific code implementation, please refer to:
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[rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py)
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[randaugment.py](../../ppocr/data/imaug/randaugment.py)
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- Training
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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:
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```bash
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├── best_accuracy.pdopt # Optimizer parameters for the best model
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├── best_accuracy.pdparams # Parameters of the best model
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├── best_accuracy.states # Metric info and epochs of the best model
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├── config.yml # Configuration file for this experiment
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├── latest.pdopt # Optimizer parameters for the latest model
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├── latest.pdparams # Parameters of the latest model
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├── latest.states # Metric info and epochs of the latest model
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└── train.log # Training log
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```
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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.
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**Note that the configuration file for prediction/evaluation must be consistent with the training.**
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### EVALUATION
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The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/cls/cls_mv3.yml` file.
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```
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export CUDA_VISIBLE_DEVICES=0
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# GPU evaluation, Global.checkpoints is the weight to be tested
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python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy
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```
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### PREDICTION
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* Training engine prediction
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Using the model trained by paddleocr, you can quickly get prediction through the following script.
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Use `Global.infer_img` to specify the path of the predicted picture or folder, and use `Global.checkpoints` to specify the weight:
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```
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# Predict English results
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python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words_en/word_10.png
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```
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Input image:
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Get the prediction result of the input image:
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```
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infer_img: doc/imgs_words_en/word_10.png
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result: ('0', 0.9999995)
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```
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