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         title: Helium
       - local: model_doc/herbert
         title: HerBERT
+      - local: model_doc/hgnet_v2
+        title: HGNet-V2
       - local: model_doc/ibert
         title: I-BERT
       - local: model_doc/jamba
@@ -691,6 +693,8 @@
         title: ConvNeXTV2
       - local: model_doc/cvt
         title: CvT
+      - local: model_doc/d_fine
+        title: D-FINE
       - local: model_doc/dab-detr
         title: DAB-DETR
       - local: model_doc/deformable_detr
 
@@ -0,0 +1,76 @@
+<!--Copyright 2025 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# D-FINE
+
+## Overview
+
+The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
+Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
+
+The abstract from the paper is the following:
+
+*We introduce D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD). 
+FDR transforms the regression process from predicting fixed coordinates to iteratively refining probability distributions, providing a fine-grained intermediate representation that significantly enhances localization accuracy. GO-LSD is a bidirectional optimization strategy that transfers localization knowledge from refined distributions to shallower layers through self-distillation, while also simplifying the residual prediction tasks for deeper layers. Additionally, D-FINE incorporates lightweight optimizations in computationally intensive modules and operations, achieving a better balance between speed and accuracy. Specifically, D-FINE-L / X achieves 54.0% / 55.8% AP on the COCO dataset at 124 / 78 FPS on an NVIDIA T4 GPU. When pretrained on Objects365, D-FINE-L / X attains 57.1% / 59.3% AP, surpassing all existing real-time detectors. Furthermore, our method significantly enhances the performance of a wide range of DETR models by up to 5.3% AP with negligible extra parameters and training costs. Our code and pretrained models: this https URL.*
+
+This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber). 
+The original code can be found [here](https://github.com/Peterande/D-FINE).
+
+## Usage tips 
+
+```python
+>>> import torch
+>>> from transformers.image_utils import load_image
+>>> from transformers import DFineForObjectDetection, AutoImageProcessor
+
+>>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+>>> image = load_image(url)
+
+>>> image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine_x_coco")
+>>> model = DFineForObjectDetection.from_pretrained("ustc-community/dfine_x_coco")
+
+>>> inputs = image_processor(images=image, return_tensors="pt")
+
+>>> with torch.no_grad():
+...     outputs = model(**inputs)
+
+>>> results = image_processor.post_process_object_detection(outputs, target_sizes=[(image.height, image.width)], threshold=0.5)
+
+>>> for result in results:
+...     for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
+...         score, label = score.item(), label_id.item()
+...         box = [round(i, 2) for i in box.tolist()]
+...         print(f"{model.config.id2label[label]}: {score:.2f} {box}")
+cat: 0.96 [344.49, 23.4, 639.84, 374.27]
+cat: 0.96 [11.71, 53.52, 316.64, 472.33]
+remote: 0.95 [40.46, 73.7, 175.62, 117.57]
+sofa: 0.92 [0.59, 1.88, 640.25, 474.74]
+remote: 0.89 [333.48, 77.04, 370.77, 187.3]
+```
+
+## DFineConfig
+
+[[autodoc]] DFineConfig
+
+## DFineModel
+
+[[autodoc]] DFineModel
+    - forward
+
+## DFineForObjectDetection
+
+[[autodoc]] DFineForObjectDetection
+    - forward
@@ -0,0 +1,46 @@
+<!--Copyright 2025 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# HGNet-V2
+
+## Overview
+
+A HGNet-V2 (High Performance GPU Net) image classification model.
+HGNet arhtictecture was proposed in [HGNET: A Hierarchical Feature Guided Network for Occupancy Flow Field Prediction](https://arxiv.org/abs/2407.01097) by
+Zhan Chen, Chen Tang, Lu Xiong
+
+The abstract from the HGNET paper is the following:
+
+*Predicting the motion of multiple traffic participants has always been one of the most challenging tasks in autonomous driving. The recently proposed occupancy flow field prediction method has shown to be a more effective and scalable representation compared to general trajectory prediction methods. However, in complex multi-agent traffic scenarios, it remains difficult to model the interactions among various factors and the dependencies among prediction outputs at different time steps. In view of this, we propose a transformer-based hierarchical feature guided network (HGNET), which can efficiently extract features of agents and map information from visual and vectorized inputs, modeling multimodal interaction relationships. Second, we design the Feature-Guided Attention (FGAT) module to leverage the potential guiding effects between different prediction targets, thereby improving prediction accuracy. Additionally, to enhance the temporal consistency and causal relationships of the predictions, we propose a Time Series Memory framework to learn the conditional distribution models of the prediction outputs at future time steps from multivariate time series. The results demonstrate that our model exhibits competitive performance, which ranks 3rd in the 2024 Waymo Occupancy and Flow Prediction Challenge.*
+
+This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber). 
+The original code can be found [here](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/backbones/hgnet_v2.py).
+
+## HGNetV2Config
+
+[[autodoc]] HGNetV2Config
+
+
+## HGNetV2Backbone
+
+[[autodoc]] HGNetV2Backbone
+    - forward
+
+
+## HGNetV2ForImageClassification
+
+[[autodoc]] HGNetV2ForImageClassification
+    - forward