first commit

2026-06-05 16:53:03 +08:00
commit 06f1fd69a6
6047 changed files with 1895387 additions and 0 deletions
--- a/docs/source/en/model_doc/vitdet.md
+++ b/docs/source/en/model_doc/vitdet.md
@@ -0,0 +1,40 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+*This model was published in HF papers on 2022-03-30 and contributed to Hugging Face Transformers on 2023-08-29.*
+
+# ViTDet
+
+
+## Overview
+
+The ViTDet model was proposed in [Exploring Plain Vision Transformer Backbones for Object Detection](https://huggingface.co/papers/2203.16527) by Yanghao Li, Hanzi Mao, Ross Girshick, Kaiming He.
+VitDet leverages the plain [Vision Transformer](vit) for the task of object detection.
+
+The abstract from the paper is the following:
+
+*We explore the plain, non-hierarchical Vision Transformer (ViT) as a backbone network for object detection. This design enables the original ViT architecture to be fine-tuned for object detection without needing to redesign a hierarchical backbone for pre-training. With minimal adaptations for fine-tuning, our plain-backbone detector can achieve competitive results. Surprisingly, we observe: (i) it is sufficient to build a simple feature pyramid from a single-scale feature map (without the common FPN design) and (ii) it is sufficient to use window attention (without shifting) aided with very few cross-window propagation blocks. With plain ViT backbones pre-trained as Masked Autoencoders (MAE), our detector, named ViTDet, can compete with the previous leading methods that were all based on hierarchical backbones, reaching up to 61.3 AP_box on the COCO dataset using only ImageNet-1K pre-training. We hope our study will draw attention to research on plain-backbone detectors.*
+
+This model was contributed by [nielsr](https://huggingface.co/nielsr).
+The original code can be found [here](https://github.com/facebookresearch/detectron2/tree/main/projects/ViTDet).
+
+Tips:
+
+- At the moment, only the backbone is available.
+
+## VitDetConfig
+
+[[autodoc]] VitDetConfig
+
+## VitDetModel
+
+[[autodoc]] VitDetModel
+    - forward