Detectors pre-trained on the COCO dataset can serve as a good pre-trained model for other datasets, e.g., CityScapes and KITTI Dataset.
This tutorial provides instruction for users to use the models provided in the [Model Zoo](../model_zoo.md) for other datasets to obatin better performance.
This tutorial provides instruction for users to use the models provided in the [Model Zoo](../model_zoo.md) for other datasets to obtain better performance.
There are two steps to finetune a model on a new dataset.
- Add support for the new dataset following [Tutorial 2: Adding New Dataset](new_dataset.md).
...
...
@@ -11,7 +11,7 @@ There are two steps to finetune a model on a new dataset.
Take the finetuning process on Cityscapes Dataset as an example, the users need to modify five parts in the config.
## Inherit base configs
To release the burdun and reduce bugs in writing the whole configs, MMDetection V2.0 support inheriting configs from multiple existing configs. To finetune a Mask RCNN model, the new config needs to inherit
To release the burden and reduce bugs in writing the whole configs, MMDetection V2.0 support inheriting configs from multiple existing configs. To finetune a Mask RCNN model, the new config needs to inherit
`_base_/models/mask_rcnn_r50_fpn.py` to build the basic structure of the model. To use the Cityscapes Dataset, the new config can also simply inherit `_base_/datasets/cityscapes_instance.py`. For runtime settings such as training schedules, the new config needs to inherit `_base_/default_runtime.py`. This configs are in the `configs` directory and the users can also choose to write the whole contents rather than use inheritance.
