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| <!--Copyright 2026 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 | ||
| 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 | ||
| 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. | ||
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| ⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be | ||
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| --> | ||
| *This model was released on 2024-04-05 and added to Hugging Face Transformers on 2026-01-23.* | ||
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| <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> | ||
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| # RF-DETR | ||
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| [RF-DETR](https://huggingface.co/papers/2407.17140) proposes a Receptive Field 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. | ||
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| The RF-DETR architecture is characterized by its simple and efficient structure: a DINOv2 Backbone, a Projector, and a | ||
| shallow DETR Decoder. | ||
| It enhances the DETR architecture for efficiency and speed using the following core modifications: | ||
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| 1. **DINOv2 Backbone**: Uses a powerful DINOv2 backbone for robust feature extraction. | ||
| 2. **Group DETR Training**: Utilizes Group-Wise One-to-Many Assignment during training to accelerate convergence. | ||
| 3. **Richer Input**: Aggregates multi-level features from the backbone and uses a C2f Projector (similarly to YOLOv8) to | ||
| pass multi-scale features. | ||
| 4. **Faster Decoder**: Employs a shallow 3-layer DETR decoder with deformable cross-attention for lower latency. | ||
| 5. **Optimized Queries**: Uses a mixed-query scheme combining learnable content queries and generated spatial queries. | ||
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| You can find all the available RF-DETR checkpoints under the [stevenbucaille](https://huggingface.co/stevenbucaille) | ||
| organization. | ||
| The original code can be found [here](https://github.com/roboflow/rf-detr). | ||
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| > [!TIP] | ||
| > This model was contributed by [stevenbucaille](https://huggingface.co/stevenbucaille). | ||
| > | ||
| > Click on the RF-DETR models in the right sidebar for more examples of how to apply RF-DETR to different object | ||
| > detection tasks. | ||
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| The example below demonstrates how to perform object detection with the [`Pipeline`] and the [`AutoModel`] class. | ||
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| <hfoptions id="usage"> | ||
| <hfoption id="Pipeline"> | ||
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| ```python | ||
| from transformers import pipeline | ||
| import torch | ||
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| pipeline = pipeline( | ||
| "object-detection", | ||
| model="stevenbucaille/rfdetr_small_60e_coco", | ||
| dtype=torch.float16, | ||
| device_map=0 | ||
|
Contributor
There was a problem hiding this comment. does this need to be included in the pipeline? |
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| ) | ||
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| pipeline("http://images.cocodataset.org/val2017/000000039769.jpg") | ||
| ``` | ||
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| </hfoption> | ||
| <hfoption id="AutoModel"> | ||
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| ```python | ||
| from transformers import AutoImageProcessor, AutoModelForObjectDetection | ||
| from PIL import Image | ||
| import requests | ||
| import torch | ||
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| url = "http://images.cocodataset.org/val2017/000000039769.jpg" | ||
| image = Image.open(requests.get(url, stream=True).raw) | ||
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| image_processor = AutoImageProcessor.from_pretrained("stevenbucaille/rfdetr_small") | ||
| model = AutoModelForObjectDetection.from_pretrained("stevenbucaille/rfdetr_small") | ||
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| # prepare image for the model | ||
| inputs = image_processor(images=image, return_tensors="pt") | ||
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| with torch.no_grad(): | ||
| outputs = model(**inputs) | ||
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| results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3) | ||
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| 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}") | ||
| ``` | ||
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| </hfoption> | ||
| </hfoptions> | ||
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| ## Resources | ||
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| - Scripts for finetuning [`RfDetrForObjectDetection`] 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). | ||
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| ## RfDetrConfig | ||
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| [[autodoc]] RfDetrConfig | ||
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| ## RfDetrDinov2Config | ||
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| [[autodoc]] RfDetrDinov2Config | ||
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| ## RfDetrModel | ||
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| [[autodoc]] RfDetrModel | ||
| - forward | ||
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| ## RfDetrForObjectDetection | ||
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| [[autodoc]] RfDetrForObjectDetection | ||
| - forward | ||
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| ## RfDetrForInstanceSegmentation | ||
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| [[autodoc]] RfDetrForInstanceSegmentation | ||
| - forward | ||
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| ## RfDetrDinov2Backbone | ||
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| [[autodoc]] RfDetrDinov2Backbone | ||
| - forward | ||
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For your question: yes, all
WeigtRenamingops live inconversion_mapping.pyfor now, as a matter of fact we try to get rid of conversion scripts entirely. Reviewing to see if we can absorb it