diff --git a/docs/en/user_guides/dataset_prepare.md b/docs/en/user_guides/dataset_prepare.md index 59b0052d..088aaf3f 100644 --- a/docs/en/user_guides/dataset_prepare.md +++ b/docs/en/user_guides/dataset_prepare.md @@ -84,25 +84,23 @@ data/icdar2015 ### Single Dataset Training -When training or evaluating a model on new datasets, we need to write the dataset config where the image path, annotation path, and image prefix are set. The path `configs/xxx/_base_/datasets/` is pre-configured with the commonly used datasets in MMOCR (if you use `prepare_dataset.py` to prepare dataset, this config will be generated automatically), here we take the ICDAR 2015 dataset as an example (see `configs/_base_/det_datasets/icdar2015.py`). +When training or evaluating a model on new datasets, we need to write the dataset config where the image path, annotation path, and image prefix are set. The path `configs/xxx/_base_/datasets/` is pre-configured with the commonly used datasets in MMOCR (if you use `prepare_dataset.py` to prepare dataset, this config will be generated automatically), here we take the ICDAR 2015 dataset as an example (see `configs/textdet/_base_/datasets/icdar2015.py`). ```Python -ic15_det_data_root = 'data/icdar2015' # dataset root path +icdar2015_textdet_data_root = 'data/icdar2015' # dataset root path # Train set config icdar2015_textdet_train = dict( type='OCRDataset', - data_root=ic15_det_data_root, # dataset root path - ann_file='instances_training.json', # name of annotation - data_prefix=dict(img_path='imgs/'), # prefix of image path + data_root=icdar2015_textdet_data_root, # dataset root path + ann_file='textdet_train.json', # name of annotation filter_cfg=dict(filter_empty_gt=True, min_size=32), # filtering empty images pipeline=None) # Test set config icdar2015_textdet_test = dict( type='OCRDataset', - data_root=ic15_det_data_root, - ann_file='instances_test.json', - data_prefix=dict(img_path='imgs/'), + data_root=icdar2015_textdet_data_root, + ann_file='textdet_test.json', test_mode=True, pipeline=None) ``` diff --git a/docs/en/user_guides/visualization.md b/docs/en/user_guides/visualization.md index 40591be2..458def97 100644 --- a/docs/en/user_guides/visualization.md +++ b/docs/en/user_guides/visualization.md @@ -36,7 +36,7 @@ Based on the above example, we can see that the configuration of `Visualizer` co MMOCR uses the local visualization backend [`LocalVisBackend`](mmengine.visualization.LocalVisBackend) by default, and the model loss, learning rate, model evaluation accuracy and visualization The information stored in `VisualizerHook` and `LoggerHook`, including loss, learning rate, evaluation accuracy will be saved to the `{work_dir}/{config_name}/{time}/{vis_data}` folder by default. In addition, MMOCR also supports other common visualization backends, such as `TensorboardVisBackend` and `WandbVisBackend`, and you only need to change the `vis_backends` type in the configuration file to the corresponding visualization backend. For example, you can store data to `TensorBoard` and `Wandb` by simply inserting the following code block into the configuration file. ```Python -_base_.Visualizer.vis_backends = [ +_base_.visualizer.vis_backends = [ dict(type='LocalVisBackend'), dict(type='TensorboardVisBackend'), dict(type='WandbVisBackend'),] diff --git a/docs/zh_cn/user_guides/dataset_prepare.md b/docs/zh_cn/user_guides/dataset_prepare.md index c684738e..6e6a1977 100644 --- a/docs/zh_cn/user_guides/dataset_prepare.md +++ b/docs/zh_cn/user_guides/dataset_prepare.md @@ -84,25 +84,23 @@ data/icdar2015 ### 单数据集训练 -在使用新的数据集时，我们需要对其图像、标注文件的路径等基础信息进行配置。`configs/xxx/_base_/datasets/` 路径下已预先配置了 MMOCR 中常用的数据集（当你使用 `prepare_dataset.py` 来准备数据集时，这个配置文件通常会在数据集准备就绪后自动生成），这里我们以 ICDAR 2015 数据集为例（见 `configs/_base_/det_datasets/icdar2015.py`）： +在使用新的数据集时，我们需要对其图像、标注文件的路径等基础信息进行配置。`configs/xxx/_base_/datasets/` 路径下已预先配置了 MMOCR 中常用的数据集（当你使用 `prepare_dataset.py` 来准备数据集时，这个配置文件通常会在数据集准备就绪后自动生成），这里我们以 ICDAR 2015 数据集为例（见 `configs/textdet/_base_/datasets/icdar2015.py`）： ```Python -ic15_det_data_root = 'data/icdar2015' # 数据集根目录 +icdar2015_textdet_data_root = 'data/icdar2015' # 数据集根目录 # 训练集配置 icdar2015_textdet_train = dict( type='OCRDataset', - data_root=ic15_det_data_root, # 数据根目录 - ann_file='instances_training.json', # 标注文件名称 - data_prefix=dict(img_path='imgs/'), # 图片路径前缀 + data_root=icdar2015_textdet_data_root, # 数据根目录 + ann_file='textdet_train.json', # 标注文件名称 filter_cfg=dict(filter_empty_gt=True, min_size=32), # 数据过滤 pipeline=None) # 测试集配置 icdar2015_textdet_test = dict( type='OCRDataset', - data_root=ic15_det_data_root, - ann_file='instances_test.json', - data_prefix=dict(img_path='imgs/'), + data_root=icdar2015_textdet_data_root, + ann_file='textdet_test.json', test_mode=True, pipeline=None) ``` diff --git a/docs/zh_cn/user_guides/visualization.md b/docs/zh_cn/user_guides/visualization.md index 262452e1..043b7ddc 100644 --- a/docs/zh_cn/user_guides/visualization.md +++ b/docs/zh_cn/user_guides/visualization.md @@ -36,7 +36,7 @@ visualizer = dict( MMOCR 默认使用本地可视化后端 [`LocalVisBackend`](mmengine.visualization.LocalVisBackend)，`VisualizerHook` 和`LoggerHook` 中存储的模型损失、学习率、模型评估精度以及可视化结果等信息将被默认保存至`{work_dir}/{config_name}/{time}/{vis_data}` 文件夹。此外，MMOCR 也支持其它常用的可视化后端，如 `TensorboardVisBackend` 以及 `WandbVisBackend`用户只需要将配置文件中的 `vis_backends` 类型修改为对应的可视化后端即可。例如，用户只需要在配置文件中插入以下代码块，即可将数据存储至 `TensorBoard` 以及 `WandB`中。 ```Python -_base_.Visualizer.vis_backends = [ +_base_.visualizer.vis_backends = [ dict(type='LocalVisBackend'), dict(type='TensorboardVisBackend'), dict(type='WandbVisBackend'),]