## 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 /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_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: [randaugment.py.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/cls/randaugment.py) [img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.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 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 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. The default prediction picture is stored in `infer_img`, and the weight is specified via `-o Global.checkpoints`: ``` # Predict English results python3 tools/infer_rec.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg ``` Input image: ![](../imgs_words/en/word_1.png) Get the prediction result of the input image: ``` infer_img: doc/imgs_words/en/word_1.png scores: [[0.93161047 0.06838956]] label: [0] ```