| # Convert your own dataset for DeepLab2 framework | |
| You may want to train DeepLab2 on your own dataset. Here, we provide some | |
| guidances and hopefully that will facillitate the preparation process. | |
| 1. Prepare your own dataset. | |
| * **Images** should be stored either in `jpg` or `png` format. | |
| * **Annotations** should be stored either in `png` or `raw` format. The | |
| DeepLab2 framework assumes the panoptic label format (i.e., | |
| `panoptic_label = semantic_label * label_divisor + instance_id`, where the | |
| `label_divisor` should be larger than the maximum number of instances per | |
| image). The `raw` format refers to the case where we could save the panoptic | |
| annotations in the int32 array (e.g., int32_numpy_array.tostring()). | |
| 2. Convert the dataset to TFRecord. | |
| * Update our provided example code (e.g., | |
| [build_step_data.py](../../data/build_step_data.py))to convert your dataset | |
| to TFRecord. | |
| 3. Modify the `dataset.py` (path: `${DEEPLAB2}/data/dataset.py`) to provide | |
| your dataset information. | |
| * Set the `panoptic_label_divisor` (i.e., the `label_divisor` above) | |
| correctly. Its value should be larger than the maximum number of instances | |
| that could appear per image in your dataset. | |
| * Set the `ignore_label` properly. Pixels annotated with `ignore_label` | |
| are not used during both training and evaluation. If your dataset does not | |
| contain the `ignore_label` annotations, you could simply set it to be a | |
| large value (e.g., 255 as for | |
| [Cityscapes](https://www.cityscapes-dataset.com/)). | |
| * Set the `class_has_instance_list` properly. The variable specifies | |
| which class belongs to the `thing` class (i.e., countable objects such as | |
| people, cars). | |
| * Set the colormap (for visualization) properly. You may also need to | |
| define your own colormap (see `${DEEPLAB2}/trainer/vis_utils.py`). | |
| 4. Prepare the experiment config. | |
| * Update our provided example configs (path: | |
| `${DEEPLAB2}/configs/${DATASET}/${MODEL}/${BACKBONE}`) for your use | |
| case. A few things that may worth your attention: | |
| * Set the `crop_size` correctly for both training and evaluation. See | |
| Q2 in [FAQ](../faq.md) for more details. | |
| * Tune the config flags for your dataset (e.g., `base_learning_rate`, | |
| `training_number_of_step`, and so on). | |
| Finally, if your dataset only contains semantic segmentation annotations, | |
| you could still use DeepLab2 framework with some minor changes: | |
| 1. Set `panoptic_label_divisor=None` in dataset.py (we also provide one | |
| example in dataset.py, where we only train with semantic segmentation on | |
| Cityscapes). | |
| 2. Have a config similar to | |
| `${DEEPLAB2}/configs/cityscapes/panoptic_deeplab/ | |
| resnet50_os32_semseg.textproto`, where the instance branch is not | |
| initiated. | |
| At this point, you are good to go! Enjoy training DeepLab2! | |