This article provides a comprehensive guide for the PaddleOCR text recognition task, covering the entire workflow including data preparation, model training, fine-tuning, evaluation, and prediction, with detailed explanations for each phase.
-`lmdb`: Used for training with datasets stored in LMDB format (LMDBDataSet);
-`General Data`: Used for training with datasets stored in text files (SimpleDataSet);
The default storage path for training data is `PaddleOCR/train_data`. If you already have a dataset on your disk, simply create a symbolic link to the dataset directory:
It is recommended to place the training images in the same folder and record the image paths and labels in a txt file (`rec_gt_train.txt`). The content of the txt file should be as follows:
**Note:** In the txt file, please use `\t` to separate the image path and the label. Using any other separator will cause errors during training.
```text
" Image Filename Image Label "
train_data/rec/train/word_001.jpg Simple and reliable
train_data/rec/train/word_002.jpg Making the complex world simpler with technology
...
```
The final structure of the training dataset should look like this:
```text
|-train_data
|-rec
|- rec_gt_train.txt
|- train
|- word_001.png
|- word_002.jpg
|- word_003.jpg
| ...
```
In addition to the single-image-per-line format described above, PaddleOCR also supports training on data augmented offline. To avoid sampling the same sample multiple times in the same batch, we can list image paths with the same label on one line. During training, PaddleOCR will randomly select one image from the list. The corresponding format of the label file is as follows:
```text
["11.jpg", "12.jpg"] Simple and reliable
["21.jpg", "22.jpg", "23.jpg"] Making the complex world simpler with technology
3.jpg ocr
```
In the above example, both "11.jpg" and "12.jpg" have the same label `Simple and reliable`. During training, one of these images will be randomly chosen for training.
- Validation Dataset
Similarly to the training dataset, the validation dataset should also provide a folder containing all the images (test) and a `rec_gt_test.txt` file. The structure of the validation dataset is as follows:
```text
|-train_data
|-rec
|- rec_gt_test.txt
|- test
|- word_001.jpg
|- word_002.jpg
|- word_003.jpg
| ...
```
### 1.3. Data Download
- ICDAR2015
If you don't have a dataset locally, you can download the [ICDAR2015](http://rrc.cvc.uab.es/?ch=4&com=downloads) dataset from the official website for quick testing. You can also refer to [DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) to download the LMDB formatted dataset needed for benchmarking.
If you're using the public ICDAR2015 dataset, PaddleOCR provides a label file for training the ICDAR2015 dataset. You can download it as follows:
The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded using the following two methods.
Finally, a dictionary ({word_dict_name}.txt) needs to be provided so that when the model is trained, all the characters that appear can be mapped to the dictionary index.
Therefore, the dictionary needs to contain all the characters that you want to be recognized correctly. {word_dict_name}.txt needs to be written in the following format and saved in the `utf-8` encoding format:
```text linenums="1"
l
d
a
d
r
n
```
In `word_dict.txt`, there is a single word in each line, which maps characters and numeric indexes together, e.g "and" will be mapped to [2 5 1]
Currently, the multilingual models are still in the demo stage, and we are continuously improving the models and adding new languages. **We highly welcome you to provide dictionaries and fonts for other languages**. If you are willing, you can submit your dictionary files to the [dict](../../ppocr/utils/dict) directory, and we will credit you in the repo.
To customize the dict file, please modify the `character_dict_path` field in `configs/rec/rec_icdar15_train.yml`.
If you need to customize dic file, please add character_dict_path field in configs/rec/rec_icdar15_train.yml to point to your dictionary path. And set character_type to ch.
PaddleOCR provides a variety of data augmentation methods. All the augmentation methods are enabled by default.
The default perturbation methods are: cvtColor, blur, jitter, Gasuss noise, random crop, perspective, color reverse, TIA augmentation.
Each disturbance method is selected with a 40% probability during the training process. For specific code implementation, please refer to: [rec_img_aug.py](../../ppocr/data/imaug/rec_img_aug.py)
PaddleOCR provides training scripts, evaluation scripts, and prediction scripts. This section will take the PP-OCRv3 English recognition model as an example:
PaddleOCR supports alternating training and evaluation. You can modify `eval_batch_step` in `configs/rec/rec_icdar15_train.yml` to set the evaluation frequency. By default, it is evaluated every 500 iter and the best acc model is saved under `output/rec_CRNN/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.
- Tip: You can use the `-c` parameter to select multiple model configurations under the `configs/rec/` path for training. The recognition algorithms supported at [rec_algorithm](../../algorithm/overview.en.md):
For training Chinese data, it is recommended to use
[ch_PP-OCRv3_rec_distillation.yml](https://github.com/PaddlePaddle/PaddleOCR/tree/main/configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml). If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:
# Type of dataset，we support LMDBDataSet and SimpleDataSet
name: SimpleDataSet
# Path of dataset
data_dir: ./train_data/
# Path of train list
label_file_list: ["./train_data/train_list.txt"]
transforms:
...
- RecResizeImg:
# Modify image_shape to fit long text
image_shape: [3, 48, 320]
...
loader:
...
# Train batch_size for Single card
batch_size_per_card: 256
...
Eval:
dataset:
# Type of dataset，we support LMDBDataSet and SimpleDataSet
name: SimpleDataSet
# Path of dataset
data_dir: ./train_data
# Path of eval list
label_file_list: ["./train_data/val_list.txt"]
transforms:
...
- RecResizeImg:
# Modify image_shape to fit long text
image_shape: [3, 48, 320]
...
loader:
# Eval batch_size for Single card
batch_size_per_card: 256
...
```
**Note that the configuration file for prediction/evaluation must be consistent with the training.**
### 2.2 Load Trained Model and Continue Training
If you expect to load trained model and continue the training again, you can specify the parameter `Global.checkpoints` as the model path to be loaded.
**Note**: The priority of `Global.checkpoints` is higher than that of `Global.pretrained_model`, that is, when two parameters are specified at the same time, the model specified by `Global.checkpoints` will be loaded first. If the model path specified by `Global.checkpoints` is wrong, the one specified by `Global.pretrained_model` will be loaded.
### 2.3 Training with New Backbone
The network part completes the construction of the network, and PaddleOCR divides the network into four parts, which are under [ppocr/modeling](../../ppocr/modeling). The data entering the network will pass through these four parts in sequence(transforms->backbones->
necks->heads).
```bash linenums="1"
├── architectures # Code for building network
├── transforms # Image Transformation Module
├── backbones # Feature extraction module
├── necks # Feature enhancement module
└── heads # Output module
```
If the Backbone to be replaced has a corresponding implementation in PaddleOCR, you can directly modify the parameters in the `Backbone` part of the configuration yml file.
However, if you want to use a new Backbone, an example of replacing the backbones is as follows:
1. Create a new file under the [ppocr/modeling/backbones](../../ppocr/modeling/backbones) folder, such as my_backbone.py.
2. Add code in the my_backbone.py file, the sample code is as follows:
3. Import the added module in the [ppocr/modeling/backbones/\__init\__.py](https://github.com/PaddlePaddle/PaddleOCR/blob/main/ppocr/modeling/backbones/__init__.py) file.
If you want to speed up your training further, you can use [Auto Mixed Precision Training](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/performance_improving/amp_en.html), taking a single machine and a single gpu as an example, the commands are as follows:
During multi-machine multi-gpu training, use the `--ips` parameter to set the used machine IP address, and the `--gpus` parameter to set the used GPU ID:
1. When using multi-machine and multi-gpu training, you need to replace the ips value in the above command with the address of your machine, and the machines need to be able to ping each other.
2. Training needs to be launched separately on multiple machines. The command to view the ip address of the machine is `ifconfig`.
3. For more details about the distributed training speedup ratio, please refer to [Distributed Training Tutorial](../blog/distributed_training.en.md).
Knowledge distillation is supported in PaddleOCR for text recognition training process. For more details, please refer to [doc](../model_compress/knowledge_distillation.en.md).
# Type of dataset，we support LMDBDataSet and SimpleDataSet
name: SimpleDataSet
# Path of dataset
data_dir: ./train_data
# Path of eval list
label_file_list: ["./train_data/french_val.txt"]
...
```
### 2.8 Training on other platform(Windows/macOS/Linux DCU)
- Windows GPU/CPU
The Windows platform is slightly different from the Linux platform:
Windows platform only supports `single gpu` training and inference, specify GPU for training `set CUDA_VISIBLE_DEVICES=0`
On the Windows platform, DataLoader only supports single-process mode, so you need to set `num_workers` to 0;
- macOS
GPU mode is not supported, you need to set `use_gpu` to False in the configuration file, and the rest of the training evaluation prediction commands are exactly the same as Linux GPU.
- Linux DCU
Running on a DCU device requires setting the environment variable `export HIP_VISIBLE_DEVICES=0,1,2,3`, and the rest of the training and evaluation prediction commands are exactly the same as the Linux GPU.
## 2.9 Fine-tuning
In actual use, it is recommended to load the official pre-trained model and fine-tune it in your own data set. For the fine-tuning method of the recognition model, please refer to: [Model Fine-tuning Tutorial](./finetune.en.md).
The model parameters during training are saved in the `Global.save_model_dir` directory by default. When evaluating indicators, you need to set `Global.checkpoints` to point to the saved parameter file. The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml` file.
```bash linenums="1"
# GPU evaluation, Global.checkpoints is the weight to be tested
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 trained weight is specified via `-o Global.checkpoints`:
According to the `save_model_dir` and `save_epoch_step` fields set in the configuration file, the following parameters will be saved:
```text linenums="1"
output/rec/
├── best_accuracy.pdopt
├── best_accuracy.pdparams
├── best_accuracy.states
├── config.yml
├── iter_epoch_3.pdopt
├── iter_epoch_3.pdparams
├── iter_epoch_3.states
├── latest.pdopt
├── latest.pdparams
├── latest.states
└── train.log
```
Among them, best_accuracy._is the best model on the evaluation set; iter_epoch_x._ is the model saved at intervals of `save_epoch_step`; latest.* is the model of the last epoch.
The configuration file used for prediction must be consistent with the training. For example, you completed the training of the Chinese model with `python3 tools/train.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml`, you can use the following command to predict the Chinese model:
The inference model is a "frozen" version of the model, where both the model structure and model parameters are saved in a file. It is typically used for prediction and deployment scenarios.
In contrast, the **checkpoint model** only saves the model's parameters and is mostly used for training resumption, etc. Compared to the checkpoint model, the inference model also includes the model structure information, which makes it more efficient for deployment, inference acceleration, and flexible integration with systems.
If you have a model trained on your own dataset with a different dictionary file, please make sure that you modify the `character_dict_path` in the configuration file to your dictionary file path.
After the conversion is successful, there are three files in the model save directory:
```text linenums="1"
inference/en_PP-OCRv3_rec/
├── inference.pdiparams # The parameter file of recognition inference model
├── inference.pdiparams.info # The parameter information of recognition inference model, which can be ignored
└── inference.pdmodel # The program file of recognition model
├── inference.pdiparams # Model parameter file for the inference model
└── inference.json # Program file for the inference model
```
### Custom Model Inference
If you modified the text dictionary during training, you must specify the path to the custom dictionary when using the inference model for prediction. For more information about configuring and explaining inference hyperparameters, refer to the [Inference Hyperparameters Explanation Tutorial](../blog/inference_args.md).
**A**: This is a common issue. It typically arises due to differences in the preprocessing and postprocessing parameters used during training and inference. To troubleshoot, check whether the preprocessing, postprocessing, and prediction settings in the configuration file used for training match those used during inference.
