huggingface · NielsRogge · May 2, 2022 · Apr 13, 2022 · Apr 13, 2022 · Apr 13, 2022
diff --git a/README.md b/README.md
@@ -340,6 +340,7 @@ AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Ch
 1. **[XLNet](https://huggingface.co/docs/transformers/model_doc/xlnet)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
 1. **[XLSR-Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
 1. **[XLS-R](https://huggingface.co/docs/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
+1. **[YOLOS](https://huggingface.co/docs/transformers/main/model_doc/yolos)** (from Huazhong University of Science & Technology) released with the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
 1. **[YOSO](https://huggingface.co/docs/transformers/model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
 1. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR.
 

diff --git a/README_ko.md b/README_ko.md
@@ -318,6 +318,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[XLNet](https://huggingface.co/docs/transformers/model_doc/xlnet)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
 1. **[XLS-R](https://huggingface.co/docs/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
 1. **[XLSR-Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
+1. **[YOLOS](https://huggingface.co/docs/transformers/main/model_doc/yolos)** (from Huazhong University of Science & Technology) released with the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
 1. **[YOSO](https://huggingface.co/docs/transformers/model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
 1. 새로운 모델을 올리고 싶나요? 우리가 **상세한 가이드와 템플릿** 으로 새로운 모델을 올리도록 도와드릴게요. 가이드와 템플릿은 이 저장소의 [`templates`](./templates) 폴더에서 확인하실 수 있습니다. [컨트리뷰션 가이드라인](./CONTRIBUTING.md)을 꼭 확인해주시고, PR을 올리기 전에 메인테이너에게 연락하거나 이슈를 오픈해 피드백을 받으시길 바랍니다. 
 

diff --git a/README_zh-hans.md b/README_zh-hans.md
@@ -342,6 +342,7 @@ conda install -c huggingface transformers
 1. **[XLNet](https://huggingface.co/docs/transformers/model_doc/xlnet)** (来自 Google/CMU) 伴随论文 [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) 由 Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le 发布。
 1. **[XLS-R](https://huggingface.co/docs/transformers/model_doc/xls_r)** (来自 Facebook AI) 伴随论文 [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) 由 Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli 发布。
 1. **[XLSR-Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/xlsr_wav2vec2)** (来自 Facebook AI) 伴随论文 [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) 由 Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli 发布。
+1. **[YOLOS](https://huggingface.co/docs/transformers/main/model_doc/yolos)** (来自 Huazhong University of Science & Technology) 伴随论文 [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) 由 Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu 发布。
 1. **[YOSO](https://huggingface.co/docs/transformers/model_doc/yoso)** (来自 the University of Wisconsin - Madison) 伴随论文 [You Only Sample (Almost) 由 Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh 发布。
 1. 想要贡献新的模型？我们这里有一份**详细指引和模板**来引导你添加新的模型。你可以在 [`templates`](./templates) 目录中找到他们。记得查看 [贡献指南](./CONTRIBUTING.md) 并在开始写 PR 前联系维护人员或开一个新的 issue 来获得反馈。
 

diff --git a/README_zh-hant.md b/README_zh-hant.md
@@ -354,6 +354,7 @@ conda install -c huggingface transformers
 1. **[XLNet](https://huggingface.co/docs/transformers/model_doc/xlnet)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
 1. **[XLS-R](https://huggingface.co/docs/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
 1. **[XLSR-Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
+1. **[YOLOS](https://huggingface.co/docs/transformers/main/model_doc/yolos)** (from Huazhong University of Science & Technology) released with the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
 1. **[YOSO](https://huggingface.co/docs/transformers/model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
 1. 想要貢獻新的模型？我們這裡有一份**詳細指引和模板**來引導你加入新的模型。你可以在 [`templates`](./templates) 目錄中找到它們。記得查看[貢獻指引](./CONTRIBUTING.md)並在開始寫 PR 前聯繫維護人員或開一個新的 issue 來獲得 feedbacks。
 

diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml
@@ -376,6 +376,8 @@
       title: XLSR-Wav2Vec2
     - local: model_doc/xls_r
       title: XLS-R
+    - local: model_doc/yolos
+      title: YOLOS
     - local: model_doc/yoso
       title: YOSO
     title: Models

diff --git a/docs/source/en/index.mdx b/docs/source/en/index.mdx
@@ -160,6 +160,7 @@ The library currently contains JAX, PyTorch and TensorFlow implementations, pret
 1. **[XLNet](model_doc/xlnet)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
 1. **[XLSR-Wav2Vec2](model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
 1. **[XLS-R](model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
+1. **[YOLOS](model_doc/yolos)** (from Huazhong University of Science & Technology) released with the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
 1. **[YOSO](model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
 
 
@@ -274,6 +275,7 @@ Flax), PyTorch, and/or TensorFlow.
 |       XLM-RoBERTa-XL        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |        XLMProphetNet        |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            XLNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|            YOLOS            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            YOSO             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 
 <!-- End table-->
diff --git a/docs/source/en/model_doc/yolos.mdx b/docs/source/en/model_doc/yolos.mdx
@@ -0,0 +1,60 @@
+<!--Copyright 2022 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.
+-->
+
+# YOLOS
+
+## Overview
+
+The YOLOS model was proposed in [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
+YOLOS proposes to just leverage the plain [Vision Transformer (ViT)](vit) for object detection, inspired by DETR. It turns out that a base-sized encoder-only Transformer can also achieve 42 AP on COCO, similar to DETR and much more complex frameworks such as Faster R-CNN.
+
+The abstract from the paper is the following:
+
+*Can Transformer perform 2D object- and region-level recognition from a pure sequence-to-sequence perspective with minimal knowledge about the 2D spatial structure? To answer this question, we present You Only Look at One Sequence (YOLOS), a series of object detection models based on the vanilla Vision Transformer with the fewest possible modifications, region priors, as well as inductive biases of the target task. We find that YOLOS pre-trained on the mid-sized ImageNet-1k dataset only can already achieve quite competitive performance on the challenging COCO object detection benchmark, e.g., YOLOS-Base directly adopted from BERT-Base architecture can obtain 42.0 box AP on COCO val. We also discuss the impacts as well as limitations of current pre-train schemes and model scaling strategies for Transformer in vision through YOLOS.*
+
+Tips:
+
+- One can use [`YolosFeatureExtractor`] for preparing images (and optional targets) for the model. Contrary to [DETR](detr), YOLOS doesn't require a `pixel_mask` to be created.
+- Demo notebooks (regarding inference and fine-tuning on custom data) can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/YOLOS).
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/yolos_architecture.png"
+alt="drawing" width="600"/> 
+
+<small> YOLOS architecture. Taken from the <a href="https://arxiv.org/abs/2106.00666">original paper</a>.</small>
+
+This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/hustvl/YOLOS).
+
+## YolosConfig
+
+[[autodoc]] YolosConfig
+
+
+## YolosFeatureExtractor
+
+[[autodoc]] YolosFeatureExtractor
+    - __call__
+    - pad
+    - post_process
+    - post_process_segmentation
+    - post_process_panoptic
+
+
+## YolosModel
+
+[[autodoc]] YolosModel
+    - forward
+
+
+## YolosForObjectDetection
+
+[[autodoc]] YolosForObjectDetection
+    - forward
diff --git a/src/transformers/__init__.py b/src/transformers/__init__.py
@@ -320,6 +320,7 @@
     "models.xlm_roberta": ["XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMRobertaConfig"],
     "models.xlm_roberta_xl": ["XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMRobertaXLConfig"],
     "models.xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"],
+    "models.yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig"],
     "models.yoso": ["YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP", "YosoConfig"],
     "onnx": [],
     "pipelines": [
@@ -551,6 +552,7 @@
     _import_structure["models.vilt"].append("ViltFeatureExtractor")
     _import_structure["models.vilt"].append("ViltProcessor")
     _import_structure["models.vit"].append("ViTFeatureExtractor")
+    _import_structure["models.yolos"].append("YolosFeatureExtractor")
 else:
     from .utils import dummy_vision_objects
 
@@ -1681,6 +1683,14 @@
             "load_tf_weights_in_xlnet",
         ]
     )
+    _import_structure["models.yolos"].extend(
+        [
+            "YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "YolosForObjectDetection",
+            "YolosModel",
+            "YolosPreTrainedModel",
+        ]
+    )
     _import_structure["models.yoso"].extend(
         [
             "YOSO_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -2696,6 +2706,7 @@
     from .models.xlm_roberta import XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaConfig
     from .models.xlm_roberta_xl import XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaXLConfig
     from .models.xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
+    from .models.yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig
     from .models.yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig
 
     # Pipelines
@@ -2901,6 +2912,7 @@
         from .models.segformer import SegformerFeatureExtractor
         from .models.vilt import ViltFeatureExtractor, ViltProcessor
         from .models.vit import ViTFeatureExtractor
+        from .models.yolos import YolosFeatureExtractor
     else:
         from .utils.dummy_vision_objects import *
 
@@ -3831,6 +3843,12 @@
             XLNetPreTrainedModel,
             load_tf_weights_in_xlnet,
         )
+        from .models.yolos import (
+            YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
+            YolosForObjectDetection,
+            YolosModel,
+            YolosPreTrainedModel,
+        )
         from .models.yoso import (
             YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
             YosoForMaskedLM,

diff --git a/src/transformers/models/__init__.py b/src/transformers/models/__init__.py
@@ -135,5 +135,6 @@
     xlm_roberta,
     xlm_roberta_xl,
     xlnet,
+    yolos,
     yoso,
 )
diff --git a/src/transformers/models/auto/configuration_auto.py b/src/transformers/models/auto/configuration_auto.py
@@ -29,6 +29,7 @@
 CONFIG_MAPPING_NAMES = OrderedDict(
     [
         # Add configs here
+        ("yolos", "YolosConfig"),
         ("tapex", "BartConfig"),
         ("dpt", "DPTConfig"),
         ("decision_transformer", "DecisionTransformerConfig"),
@@ -138,6 +139,7 @@
 CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
     [
         # Add archive maps here)
+        ("yolos", "YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("dpt", "DPT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("glpn", "GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("maskformer", "MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
@@ -231,6 +233,7 @@
 MODEL_NAMES_MAPPING = OrderedDict(
     [
         # Add full (and cased) model names here
+        ("yolos", "YOLOS"),
         ("tapex", "TAPEX"),
         ("dpt", "DPT"),
         ("decision_transformer", "Decision Transformer"),

diff --git a/src/transformers/models/auto/feature_extraction_auto.py b/src/transformers/models/auto/feature_extraction_auto.py
@@ -61,6 +61,7 @@
         ("data2vec-vision", "BeitFeatureExtractor"),
         ("dpt", "DPTFeatureExtractor"),
         ("glpn", "GLPNFeatureExtractor"),
+        ("yolos", "YolosFeatureExtractor"),
     ]
 )
 

diff --git a/src/transformers/models/auto/modeling_auto.py b/src/transformers/models/auto/modeling_auto.py
@@ -28,6 +28,7 @@
 MODEL_MAPPING_NAMES = OrderedDict(
     [
         # Base model mapping
+        ("yolos", "YolosModel"),
         ("dpt", "DPTModel"),
         ("decision_transformer", "DecisionTransformerModel"),
         ("glpn", "GLPNModel"),
@@ -386,6 +387,7 @@
 MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES = OrderedDict(
     [
         # Model for Object Detection mapping
+        ("yolos", "YolosForObjectDetection"),
         ("detr", "DetrForObjectDetection"),
     ]
 )
-Original file line number
+Diff line change
@@ Expand Up / @@ -135,5 +135,6 @@ @@
         xlm_roberta,
         xlm_roberta_xl,
         xlnet,
+        yolos,
         yoso,
     )