Adding RTDETRv2

docs/source/en/_toctree.yml

@@ -709,6 +709,8 @@
         title: ResNet
       - local: model_doc/rt_detr
         title: RT-DETR
+      - local: model_doc/rt_detr_v2
+        title: RT-DETRv2
       - local: model_doc/segformer
         title: SegFormer
       - local: model_doc/seggpt

docs/source/en/index.md

@@ -305,6 +305,7 @@ Flax), PyTorch, and/or TensorFlow.
 |                      [RoFormer](model_doc/roformer)                      |       ✅        |         ✅         |      ✅      |
 |                       [RT-DETR](model_doc/rt_detr)                       |       ✅        |         ❌         |      ❌      |
 |                [RT-DETR-ResNet](model_doc/rt_detr_resnet)                |       ✅        |         ❌         |      ❌      |
+|                    [RT-DETRv2](model_doc/rt_detr_v2)                     |       ✅        |         ❌         |      ❌      |
 |                          [RWKV](model_doc/rwkv)                          |       ✅        |         ❌         |      ❌      |
 |                           [SAM](model_doc/sam)                           |       ✅        |         ✅         |      ❌      |
 |                  [SeamlessM4T](model_doc/seamless_m4t)                   |       ✅        |         ❌         |      ❌      |

docs/source/en/model_doc/rt_detr_v2.md

@@ -0,0 +1,97 @@
+<!--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.
+
+-->
+
+# RT-DETRv2
+
+## Overview
+
+The RT-DETRv2 model was proposed in [RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer](https://arxiv.org/abs/2407.17140) by Wenyu Lv, Yian Zhao, Qinyao Chang, Kui Huang, Guanzhong Wang, Yi Liu.
+
+RT-DETRv2 refines RT-DETR by introducing selective multi-scale feature extraction, a discrete sampling operator for broader deployment compatibility, and improved training strategies like dynamic data augmentation and scale-adaptive hyperparameters. These changes enhance flexibility and practicality while maintaining real-time performance.
+
+The abstract from the paper is the following:
+
+*In this report, we present RT-DETRv2, an improved Real-Time DEtection TRansformer (RT-DETR). RT-DETRv2 builds upon the previous state-of-the-art real-time detector, RT-DETR, and opens up a set of bag-of-freebies for flexibility and practicality, as well as optimizing the training strategy to achieve enhanced performance. To improve the flexibility, we suggest setting a distinct number of sampling points for features at different scales in the deformable attention to achieve selective multi-scale feature extraction by the decoder. To enhance practicality, we propose an optional discrete sampling operator to replace the grid_sample operator that is specific to RT-DETR compared to YOLOs. This removes the deployment constraints typically associated with DETRs. For the training strategy, we propose dynamic data augmentation and scale-adaptive hyperparameters customization to improve performance without loss of speed.*
+
+This model was contributed by [jadechoghari](https://huggingface.co/jadechoghari).
+The original code can be found [here](https://github.com/lyuwenyu/RT-DETR).
+
+## Usage tips 
+
+This second version of RT-DETR improves how the decoder finds objects in an image. 
+
+- **better sampling** – adjusts offsets so the model looks at the right areas
+- **flexible attention** – can use smooth (bilinear) or fixed (discrete) sampling
+- **optimized processing** – improves how attention weights mix information
+
+```py
+>>> import torch
+>>> import requests
+
+>>> from PIL import Image
+>>> from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor
+
+>>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+>>> image = Image.open(requests.get(url, stream=True).raw)
+
+>>> image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r18vd")
+>>> model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r18vd")
+
+>>> 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.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: 0.97 [341.14, 25.11, 639.98, 372.89]
+cat: 0.96 [12.78, 56.35, 317.67, 471.34]
+remote: 0.95 [39.96, 73.12, 175.65, 117.44]
+sofa: 0.86 [-0.11, 2.97, 639.89, 473.62]
+sofa: 0.82 [-0.12, 1.78, 639.87, 473.52]
+remote: 0.79 [333.65, 76.38, 370.69, 187.48]
+```
+
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with RT-DETRv2.
+
+<PipelineTag pipeline="object-detection"/>
+
+- Scripts for finetuning [`RTDetrV2ForObjectDetection`] 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).
+- Notebooks for [inference](https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/RT_DETR_v2_inference.ipynb) and [fine-tuning](https://github.com/qubvel/transformers-notebooks/blob/main/notebooks/RT_DETR_v2_finetune_on_a_custom_dataset.ipynb) RT-DETRv2 on a custom dataset (🌎).
+
+
+## RTDetrV2Config
+
+[[autodoc]] RTDetrV2Config
+
+
+## RTDetrV2Model
+
+[[autodoc]] RTDetrV2Model
+    - forward
+
+## RTDetrV2ForObjectDetection
+
+[[autodoc]] RTDetrV2ForObjectDetection
+    - forward

src/transformers/__init__.py

@@ -748,6 +748,7 @@
         "RoFormerTokenizer",
     ],
     "models.rt_detr": ["RTDetrConfig", "RTDetrResNetConfig"],
+    "models.rt_detr_v2": ["RTDetrV2Config"],
     "models.rwkv": ["RwkvConfig"],
     "models.sam": [
         "SamConfig",
@@ -3454,6 +3455,9 @@
             "RTDetrResNetPreTrainedModel",
         ]
     )
+    _import_structure["models.rt_detr_v2"].extend(
+        ["RTDetrV2ForObjectDetection", "RTDetrV2Model", "RTDetrV2PreTrainedModel"]
+    )
     _import_structure["models.rwkv"].extend(
         [
             "RwkvForCausalLM",
@@ -5875,6 +5879,7 @@
         RTDetrConfig,
         RTDetrResNetConfig,
     )
+    from .models.rt_detr_v2 import RTDetrV2Config
     from .models.rwkv import RwkvConfig
     from .models.sam import (
         SamConfig,
@@ -8171,6 +8176,7 @@
             RTDetrResNetBackbone,
             RTDetrResNetPreTrainedModel,
         )
+        from .models.rt_detr_v2 import RTDetrV2ForObjectDetection, RTDetrV2Model, RTDetrV2PreTrainedModel
         from .models.rwkv import (
             RwkvForCausalLM,
             RwkvModel,

src/transformers/loss/loss_rt_detr.py

@@ -18,7 +18,6 @@
 
 from ..utils import is_scipy_available, is_vision_available, requires_backends
 from .loss_for_object_detection import (
-    _set_aux_loss,
     box_iou,
     dice_loss,
     generalized_box_iou,
@@ -35,6 +34,15 @@
     from transformers.image_transforms import center_to_corners_format
 
 
+# different for RT-DETR: not slicing the last element like in DETR one
+@torch.jit.unused
+def _set_aux_loss(outputs_class, outputs_coord):
+    # this is a workaround to make torchscript happy, as torchscript
+    # doesn't support dictionary with non-homogeneous values, such
+    # as a dict having both a Tensor and a list.
+    return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class, outputs_coord)]
+
+
 class RTDetrHungarianMatcher(nn.Module):
     """This class computes an assignment between the targets and the predictions of the network
 

src/transformers/loss/loss_utils.py

@@ -132,4 +132,5 @@ def ForTokenClassification(logits, labels, config, **kwargs):
     "GroundingDinoForObjectDetection": DeformableDetrForObjectDetectionLoss,
     "ConditionalDetrForSegmentation": DeformableDetrForSegmentationLoss,
     "RTDetrForObjectDetection": RTDetrForObjectDetectionLoss,
+    "RTDetrV2ForObjectDetection": RTDetrForObjectDetectionLoss,
 }

src/transformers/models/__init__.py

@@ -233,6 +233,7 @@
     roc_bert,
     roformer,
     rt_detr,
+    rt_detr_v2,
     rwkv,
     sam,
     seamless_m4t,

src/transformers/models/auto/configuration_auto.py

@@ -259,6 +259,7 @@
         ("roformer", "RoFormerConfig"),
         ("rt_detr", "RTDetrConfig"),
         ("rt_detr_resnet", "RTDetrResNetConfig"),
+        ("rt_detr_v2", "RTDetrV2Config"),
         ("rwkv", "RwkvConfig"),
         ("sam", "SamConfig"),
         ("seamless_m4t", "SeamlessM4TConfig"),
@@ -600,6 +601,7 @@
         ("roformer", "RoFormer"),
         ("rt_detr", "RT-DETR"),
         ("rt_detr_resnet", "RT-DETR-ResNet"),
+        ("rt_detr_v2", "RT-DETRv2"),
         ("rwkv", "RWKV"),
         ("sam", "SAM"),
         ("seamless_m4t", "SeamlessM4T"),

src/transformers/models/auto/modeling_auto.py

@@ -238,6 +238,7 @@
         ("roc_bert", "RoCBertModel"),
         ("roformer", "RoFormerModel"),
         ("rt_detr", "RTDetrModel"),
+        ("rt_detr_v2", "RTDetrV2Model"),
         ("rwkv", "RwkvModel"),
         ("sam", "SamModel"),
         ("seamless_m4t", "SeamlessM4TModel"),
@@ -897,6 +898,7 @@
         ("deta", "DetaForObjectDetection"),
         ("detr", "DetrForObjectDetection"),
         ("rt_detr", "RTDetrForObjectDetection"),
+        ("rt_detr_v2", "RTDetrV2ForObjectDetection"),
         ("table-transformer", "TableTransformerForObjectDetection"),
         ("yolos", "YolosForObjectDetection"),
     ]

src/transformers/models/rt_detr/modeling_rt_detr.py

@@ -2115,9 +2115,8 @@ def forward(
 
         loss, loss_dict, auxiliary_outputs, enc_topk_logits, enc_topk_bboxes = None, None, None, None, None
         if labels is not None:
-            if self.training and denoising_meta_values is not None:
-                enc_topk_logits = outputs.enc_topk_logits if return_dict else outputs[-5]
-                enc_topk_bboxes = outputs.enc_topk_bboxes if return_dict else outputs[-4]
+            enc_topk_logits = outputs.enc_topk_logits if return_dict else outputs[-5]
+            enc_topk_bboxes = outputs.enc_topk_bboxes if return_dict else outputs[-4]
             loss, loss_dict, auxiliary_outputs = self.loss_function(
                 logits,
                 labels,

src/transformers/models/rt_detr_v2/__init__.py

@@ -0,0 +1,29 @@
+# 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.
+
+
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_rt_detr_v2 import *
+    from .modeling_rt_detr_v2 import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)