This chapter will take you through the basic functions of MMOCR. And we assume you [installed MMOCR from source](install.md#best-practices). You may check out the [tutorial notebook](https://colab.research.google.com/github/open-mmlab/mmocr/blob/dev-1.x/demo/tutorial.ipynb) for how to perform inference, training and testing interactively.
If you are running MMOCR on a server without GUI or via SSH tunnel with X11 forwarding disabled, you may not see the pop-up window.
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A detailed description of MMOCR's inference interface can be found [here](../user_guides/inference.md)
In addition to using our well-provided pre-trained models, you can also train models on your own datasets. In the next section, we will take you through the basic functions of MMOCR by training DBNet on the mini [ICDAR 2015](https://rrc.cvc.uab.es/?ch=4&com=downloads) dataset as an example.
Since the variety of OCR dataset formats are not conducive to either switching or joint training of multiple datasets, MMOCR proposes a uniform [data format](../user_guides/dataset_prepare.md), and provides [dataset preparer](../user_guides/data_prepare/dataset_preparer.md) for commonly used OCR datasets. Usually, to use those datasets in MMOCR, you just need to follow the steps to get them ready for use.
Here, we have prepared a lite version of ICDAR 2015 dataset for demonstration purposes. Download our pre-prepared [zip](https://download.openmmlab.com/mmocr/data/icdar2015/mini_icdar2015.tar.gz) and extract it to the `data/` directory under mmocr to get our prepared image and annotation file.
In this example, we will train a DBNet using resnet18 as its backbone. Since MMOCR already has a config file for the full ICDAR 2015 dataset (`configs/textdet/dbnet/dbnet_resnet18_fpnc_1200e_icdar2015.py`), we just need to make some modifications on top of it.
We first need to modify the path to the dataset. In this config, most of the key config files are imported in `_base_`, such as the database configuration from `configs/textdet/_base_/datasets/icdar2015.py`. Open that file and replace the path pointed to by `icdar2015_textdet_data_root` in the first line with:
Also, because of the reduced dataset size, we have to reduce the number of training epochs to 400 accordingly, shorten the validation interval as well as the weight storage interval to 10 rounds, and drop the learning rate decay strategy. The following lines of configuration can be directly put into `configs/textdet/dbnet/dbnet_resnet18_fpnc_1200e_icdar2015.py` to take effect.
Here, we have rewritten the corresponding parameters in the base configuration directly through the inheritance ({external+mmengine:doc}`MMEngine: Config <advanced_tutorials/config>`) mechanism of the config. The original fields are distributed in `configs/textdet/_base_/schedules/schedule_sgd_1200e.py` and `configs/textdet/_base_/default_runtime.py`.
Before we start the training, we can also visualize the image processed by training-time [data transforms](../basic_concepts/transforms.md). It's quite simple: pass the config file we need to visualize into the [browse_dataset.py](/tools/analysis_tools/browse_dataset.py) script.
In addition to satisfying our curiosity, visualization can also help us check the parts that may affect the model's performance before training, such as problems in configs, datasets and data transforms.
Depending on the system environment, MMOCR will automatically use the best device for training. If a GPU is available, a single GPU training will be started by default. When you start to see the output of the losses, you have successfully started the training.
2022/08/22 18:42:33 - mmengine - INFO - Exp name: dbnet_resnet18_fpnc_1200e_icdar2015
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Without extra configurations, model weights will be saved to `work_dirs/dbnet_resnet18_fpnc_1200e_icdar2015/`, while the logs will be stored in `work_dirs/dbnet_resnet18_fpnc_1200e_icdar2015/TIMESTAMP/`. Next, we just need to wait with some patience for training to finish.
For advanced usage of training, such as CPU training, multi-GPU training, and cluster training, please refer to [Training and Testing](../user_guides/train_test.md).
It may not have been trained to be optimal, but it is sufficient for a demo.
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However, this value only reflects the performance of DBNet on the mini ICDAR 2015 dataset. For a comprehensive evaluation, we also need to see how it performs on out-of-distribution datasets. For example, `tests/data/det_toy_dataset` is a very small real dataset that we can use to verify the actual performance of DBNet.
Before testing, we also need to make some changes to the location of the dataset. Open `configs/textdet/_base_/datasets/icdar2015.py` and change `data_root` of `icdar2015_textdet_test` to `tests/data/det_toy_dataset`:
For advanced usage of testing, such as CPU testing, multi-GPU testing, and cluster testing, please refer to [Training and Testing](../user_guides/train_test.md).
We can also visualize its prediction output in `test.py`. You can open a pop-up visualization window with the `show` parameter; and can also specify the directory where the prediction result images are exported with the `show-dir` parameter.
The true labels and predicted values are displayed in a tiled fashion in the visualization results. The green boxes in the left panel indicate the true labels and the red boxes in the right panel indicate the predicted values.
