Add LWDetr model

docs/source/en/_toctree.yml

@@ -817,6 +817,8 @@
         title: LeViT
       - local: model_doc/lightglue
         title: LightGlue
+      - local: model_doc/lw_detr
+        title: LW-DETR
       - local: model_doc/mask2former
         title: Mask2Former
       - local: model_doc/maskformer

docs/source/en/model_doc/lw_detr.md

@@ -0,0 +1,127 @@
+<!--Copyright 2026 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.
+
+-->
+*This model was released on 2024-04-05 and added to Hugging Face Transformers on 2026-01-10.* 
+
+<div style="float: right;">
+ <div class="flex flex-wrap space-x-1">
+  <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+ </div>
+</div>
+
+# LW-DETR
+
+[LW-DETR](https://huggingface.co/papers/2407.17140) proposes a light-weight Detection Transformer (DETR) architecture designed to compete with and surpass the dominant YOLO series for real-time object detection. It achieves a new state-of-the-art balance between speed (latency) and accuracy (mAP) by combining recent transformer advances with efficient design choices.
+
+The LW-DETR architecture is characterized by its simple and efficient structure: a plain ViT Encoder, a Projector, and a shallow DETR Decoder.
+It enhances the DETR architecture for efficiency and speed using the following core modifications:
+1. Efficient ViT Encoder: Uses a plain ViT with interleaved window/global attention and a window-major organization to drastically reduce attention complexity and latency.
+2. Richer Input: Aggregates multi-level features from the encoder and uses a C2f Projector (YOLOv8) to pass two-scale features ($1/8$ and $1/32$).
+3. Faster Decoder: Employs a shallow 3-layer DETR decoder with deformable cross-attention for lower latency and faster convergence.
+4. Optimized Queries: Uses a mixed-query scheme combining learnable content queries and generated spatial queries.
+
+You can find all the available Deformable DETR checkpoints under the [stevenbucaille](https://huggingface.co/stevenbucaille) organization.
+The original code can be found [here](https://github.com/Atten4Vis/LW-DETR).
+
+> [!TIP]
+> This model was contributed by [stevenbucaille](https://huggingface.co/stevenbucaille).
+>
+> Click on the LW-DETR models in the right sidebar for more examples of how to apply LW-DETR to different object detection tasks.
+
+
+The example below demonstrates how to perform object detection with the [`Pipeline`] and the [`AutoModel`] class.
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```python
+from transformers import pipeline
+import torch
+
+pipeline = pipeline(
+    "object-detection", 
+    model="stevenbucaille/lwdetr_small_60e_coco",
+    dtype=torch.float16,
+    device_map=0
+)
+
+pipeline("http://images.cocodataset.org/val2017/000000039769.jpg")
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```python
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
+from PIL import Image
+import requests
+import torch
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+image_processor = AutoImageProcessor.from_pretrained("stevenbucaille/lwdetr_small_60e_coco")
+model = AutoModelForObjectDetection.from_pretrained("stevenbucaille/lwdetr_small_60e_coco")
+
+# prepare image for the model
+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=torch.tensor([image.size[::-1]]), threshold=0.3)
+
+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}")
+```
+
+</hfoption>
+</hfoptions>
+
+
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LwDetr.
+
+<PipelineTag pipeline="object-detection"/>
+
+- Scripts for finetuning [`LwDetrForObjectDetection`] with [`Trainer`] or [Accelerate](https://huggingface.co/docs/accelerate/index) can be found [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/object-detection).
+- See also: [Object detection task guide](../tasks/object_detection).
+
+## LwDetrConfig
+
+[[autodoc]] LwDetrConfig
+
+## LwDetrViTConfig
+
+[[autodoc]] LwDetrViTConfig
+
+## LwDetrModel
+
+[[autodoc]] LwDetrModel
+    - forward
+
+## LwDetrForObjectDetection
+
+[[autodoc]] LwDetrForObjectDetection
+    - forward
+
+## LwDetrViTBackbone
+
+[[autodoc]] LwDetrViTBackbone
+    - forward