Move the "data" section

docs/usage/train/config.md

@@ -80,16 +80,6 @@ folder/
 | `training.load_epoch`    | Model to load. Should be either `"best"` (evaluation) or `last` (training). | `str`        | `"last"` |
 | `training.lr_schedulers` | Learning rate schedulers.                                                   | custom class |          |
 
-### Data
-
-| Name                           | Description                                                | Type   | Default                                              |
-| ------------------------------ | ---------------------------------------------------------- | ------ | ---------------------------------------------------- |
-| `training.data.batch_size`     | Mini-batch size for the training loop.                     | `int`  | `2`                                                  |
-| `training.data.load_in_memory` | Load all images in CPU memory.                             | `bool` | `True`                                               |
-| `training.data.worker_per_gpu` | Number of parallel processes per gpu for data loading.     | `int`  | `4`                                                  |
-| `training.data.preprocessings` | List of pre-processing functions to apply to input images. | `list` | (see [dedicated section](#data-preprocessing))       |
-| `training.data.augmentation`   | Whether to use data augmentation on the training set.      | `bool` | `True` (see [dedicated section](#data-augmentation)) |
-
 ### Device
 
 | Name                       | Description                                                                                                                 | Type   | Default |
@@ -141,7 +131,17 @@ folder/
 | `training.transfer_learning.encoder` | Model to load for the encoder \[state_dict_name, checkpoint_path, learnable, strict\]. | `list` | `["encoder", "pretrained_models/dan_rimes_page.pt", True, True]`  |
 | `training.transfer_learning.decoder` | Model to load for the decoder \[state_dict_name, checkpoint_path, learnable, strict\]. | `list` | `["decoder", "pretrained_models/dan_rimes_page.pt", True, False]` |
 
-### Data preprocessing
+### Data
+
+| Name                           | Description                                                | Type   | Default                                         |
+| ------------------------------ | ---------------------------------------------------------- | ------ | ----------------------------------------------- |
+| `training.data.batch_size`     | Mini-batch size for the training loop.                     | `int`  | `2`                                             |
+| `training.data.load_in_memory` | Load all images in CPU memory.                             | `bool` | `True`                                          |
+| `training.data.worker_per_gpu` | Number of parallel processes per gpu for data loading.     | `int`  | `4`                                             |
+| `training.data.preprocessings` | List of pre-processing functions to apply to input images. | `list` | (see [dedicated section](#preprocessing))       |
+| `training.data.augmentation`   | Whether to use data augmentation on the training set.      | `bool` | `True` (see [dedicated section](#augmentation)) |
+
+#### Preprocessing
 
 Preprocessing is applied before training the network (see the [dedicated references](../../ref/ocr/managers/dataset.md)). The list of accepted transforms is defined in the [dedicated references](../../ref/ocr/transforms.md#dan.ocr.transforms.Preprocessing).
 
@@ -196,7 +196,7 @@ Usage:
 ]
 ```
 
-### Data augmentation
+#### Augmentation
 
 Augmentation transformations are applied on-the-fly during training to artificially increase data variability.