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55 lines
2.9 KiB
Plaintext
55 lines
2.9 KiB
Plaintext
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# DETA
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## Overview
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The DETA model was proposed in [NMS Strikes Back](https://arxiv.org/abs/2212.06137) by Jeffrey Ouyang-Zhang, Jang Hyun Cho, Xingyi Zhou, Philipp Krähenbühl.
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DETA (short for Detection Transformers with Assignment) improves [Deformable DETR](deformable_detr) by replacing the one-to-one bipartite Hungarian matching loss
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with one-to-many label assignments used in traditional detectors with non-maximum suppression (NMS). This leads to significant gains of up to 2.5 mAP.
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The abstract from the paper is the following:
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*Detection Transformer (DETR) directly transforms queries to unique objects by using one-to-one bipartite matching during training and enables end-to-end object detection. Recently, these models have surpassed traditional detectors on COCO with undeniable elegance. However, they differ from traditional detectors in multiple designs, including model architecture and training schedules, and thus the effectiveness of one-to-one matching is not fully understood. In this work, we conduct a strict comparison between the one-to-one Hungarian matching in DETRs and the one-to-many label assignments in traditional detectors with non-maximum supervision (NMS). Surprisingly, we observe one-to-many assignments with NMS consistently outperform standard one-to-one matching under the same setting, with a significant gain of up to 2.5 mAP. Our detector that trains Deformable-DETR with traditional IoU-based label assignment achieved 50.2 COCO mAP within 12 epochs (1x schedule) with ResNet50 backbone, outperforming all existing traditional or transformer-based detectors in this setting. On multiple datasets, schedules, and architectures, we consistently show bipartite matching is unnecessary for performant detection transformers. Furthermore, we attribute the success of detection transformers to their expressive transformer architecture.*
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Tips:
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- One can use [`DetaImageProcessor`] to prepare images and optional targets for the model.
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This model was contributed by [nielsr](https://huggingface.co/nielsr).
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The original code can be found [here](https://github.com/jozhang97/DETA).
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## DetaConfig
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[[autodoc]] DetaConfig
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## DetaImageProcessor
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[[autodoc]] DetaImageProcessor
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- preprocess
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- post_process_object_detection
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## DetaModel
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[[autodoc]] DetaModel
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- forward
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## DetaForObjectDetection
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[[autodoc]] DetaForObjectDetection
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- forward
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