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Improve detr (#12147)

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* Remove unused variables

* Improve docs

* Fix docs of segmentation masks

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
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NielsRogge
2021-06-17 16:37:54 +02:00
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parent b56848c8c8
commit 29b0aef871
3 changed files with 26 additions and 38 deletions
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9
docs/source/model_doc/detr.rst
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@@ -40,6 +40,10 @@ baselines.*
This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The original code can be found `here
<https://github.com/facebookresearch/detr>`__.
The quickest way to get started with DETR is by checking the `example notebooks
<https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETR>`__ (which showcase both inference and
fine-tuning on custom data).
Here's a TLDR explaining how :class:`~transformers.DetrForObjectDetection` works:
First, an image is sent through a pre-trained convolutional backbone (in the paper, the authors use
@@ -130,7 +134,7 @@ As a summary, consider the following table:
+---------------------------------------------+---------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------------+
| **Format of annotations to provide to** | {‘image_id’: int, | {‘image_id’: int, | {‘file_name: str, |
| :class:`~transformers.DetrFeatureExtractor` | ‘annotations’: List[Dict]}, each Dict being a COCO | ‘annotations’: [List[Dict]] } (in case of COCO detection) | ‘image_id: int, |
| | object annotation (containing keys "image_id", | | ‘segments_info’: List[Dict] } |
| | object annotation | | ‘segments_info’: List[Dict] } |
| | | or | |
| | | | and masks_path (path to directory containing PNG files of the masks) |
| | | {‘file_name’: str, | |
@@ -151,7 +155,8 @@ In short, one should prepare the data either in COCO detection or COCO panoptic
outputs of the model using one of the postprocessing methods of :class:`~transformers.DetrFeatureExtractor`. These can
be be provided to either :obj:`CocoEvaluator` or :obj:`PanopticEvaluator`, which allow you to calculate metrics like
mean Average Precision (mAP) and Panoptic Quality (PQ). The latter objects are implemented in the `original repository
<https://github.com/facebookresearch/detr>`__. See the example notebooks for more info regarding evaluation.
<https://github.com/facebookresearch/detr>`__. See the `example notebooks
<https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETR>`__ for more info regarding evaluation.
DETR specific outputs