update angle_class doc
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WenmuZhou
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@ -45,7 +45,7 @@ train_data/cls/word_002.jpg 180
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```
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|-train_data
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|-cls
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|- 和一个cls_gt_test.txt
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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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@ -62,29 +62,36 @@ PaddleOCR提供了训练脚本、评估脚本和预测脚本。
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*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
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```
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# 设置PYTHONPATH路径
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export PYTHONPATH=$PYTHONPATH:.
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# GPU训练 支持单卡，多卡训练，通过CUDA_VISIBLE_DEVICES指定卡号
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export CUDA_VISIBLE_DEVICES=0,1,2,3
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# 启动训练
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python3 tools/train.py -c configs/cls/cls_mv3.yml
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# GPU训练 支持单卡，多卡训练，通过selected_gpus指定卡号
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# 启动训练，下面的命令已经写入train.sh文件中，只需修改文件里的配置文件路径即可
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python3 -m paddle.distributed.launch --selected_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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- 数据增强
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PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入扰动，请在配置文件中设置 `distort: true`。
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PaddleOCR提供了多种数据增强方式，如果您希望在训练时加入扰动，请在配置文件中取消`Train.dataset.transforms`下的`RecAug`和`RandAugment`字段的注释。
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默认的扰动方式有：颜色空间转换(cvtColor)、模糊(blur)、抖动(jitter)、噪声(Gasuss noise)、随机切割(random crop)、透视(perspective)、颜色反转(reverse),随机数据增强(RandAugment)。
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训练过程中除随机数据增强外每种扰动方式以50%的概率被选择，具体代码实现请参考：
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[randaugment.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/cls/randaugment.py)
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[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
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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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*由于OpenCV的兼容性问题，扰动操作暂时只支持linux*
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### 训练
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PaddleOCR支持训练和评估交替进行, 可以在 `configs/cls/cls_mv3.yml` 中修改 `eval_batch_step` 设置评估频率，默认每500个iter评估一次。评估过程中默认将最佳acc模型，保存为 `output/cls_mv3/best_accuracy` 。
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PaddleOCR支持训练和评估交替进行, 可以在 `configs/cls/cls_mv3.yml` 中修改 `eval_batch_step` 设置评估频率，默认每1000个iter评估一次。训练过程中将会保存如下内容：
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```bash
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├── best_accuracy.pdopt # 最佳模型的优化器参数
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├── best_accuracy.pdparams # 最佳模型的参数
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├── best_accuracy.states # 最佳模型的指标和epoch等信息
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├── config.yml # 本次实验的配置文件
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├── latest.pdopt # 最新模型的优化器参数
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├── latest.pdparams # 最新模型的参数
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├── latest.states # 最新模型的指标和epoch等信息
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└── train.log # 训练日志
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```
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如果验证集很大，测试将会比较耗时，建议减少评估次数，或训练完再进行评估。
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@ -92,9 +99,8 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/cls/cls_mv3.yml`
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### 评估
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评估数据集可以通过`configs/cls/cls_reader.yml` 修改EvalReader中的 `label_file_path` 设置。
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评估数据集可以通过修改`configs/cls/cls_mv3.yml`文件里的`Eval.dataset.label_file_list` 字段设置。
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*注意* 评估时必须确保配置文件中 infer_img 字段为空
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```
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export CUDA_VISIBLE_DEVICES=0
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# GPU 评估， Global.checkpoints 为待测权重
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@ -107,21 +113,20 @@ python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/
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使用 PaddleOCR 训练好的模型，可以通过以下脚本进行快速预测。
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默认预测图片存储在 `infer_img` 里，通过 `-o Global.checkpoints` 指定权重：
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通过 `Global.infer_img` 指定预测图片或文件夹路径，通过 `Global.checkpoints` 指定权重：
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```
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# 预测分类结果
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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_1.png
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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/ch/word_1.jpg
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```
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预测图片：
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得到输入图像的预测结果：
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```
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infer_img: doc/imgs_words/en/word_1.png
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scores: [[0.93161047 0.06838956]]
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label: [0]
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infer_img: doc/imgs_words/ch/word_1.jpg
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result: ('0', 0.9998784)
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```
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@ -65,26 +65,35 @@ 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 CUDA_VISIBLE_DEVICES
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export CUDA_VISIBLE_DEVICES=0,1,2,3
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# Training icdar15 English data
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python3 tools/train.py -c configs/cls/cls_mv3.yml
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# GPU training Support single card and multi-card training, specify the card number through 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 --selected_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 set `distort: true` in the configuration file.
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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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[randaugment.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/cls/randaugment.py)
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[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)
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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 500 iter and the best acc model is saved under `output/cls_mv3/best_accuracy` during the evaluation process.
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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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@ -92,7 +101,7 @@ If the evaluation set is large, the test will be time-consuming. It is recommend
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### EVALUATION
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The evaluation data set can be modified via `configs/cls/cls_reader.yml` setting of `label_file_path` in EvalReader.
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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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@ -106,21 +115,20 @@ python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/
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Using the model trained by paddleocr, you can quickly get prediction through the following script.
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The default prediction picture is stored in `infer_img`, and the weight is specified via `-o Global.checkpoints`:
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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_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
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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_1.png
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scores: [[0.93161047 0.06838956]]
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label: [0]
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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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