model: Add DEIMv2 to Transformers

docs/source/en/_toctree.yml

@@ -899,6 +899,8 @@
         title: DAB-DETR
       - local: model_doc/deformable_detr
         title: Deformable DETR
+      - local: model_doc/deimv2
+        title: DEIMv2
       - local: model_doc/deit
         title: DeiT
       - local: model_doc/depth_anything

docs/source/en/model_doc/deimv2.md

@@ -0,0 +1,65 @@
+<!--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 contains specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was released on 2025-09-25 and added to Hugging Face Transformers on 2026-04-27.*
+
+# DEIMv2
+
+## Overview
+
+DEIMv2 (DETR with Improved Matching v2) was proposed in [DEIMv2: Real-Time Object Detection Meets DINOv3](https://huggingface.co/papers/2509.20787) by Shihua Huang, Yongjie Hou, Longfei Liu, Xuanlong Yu, and Xi Shen.
+
+The abstract from the paper is the following:
+
+*Driven by the simple and effective Dense O2O, DEIM demonstrates faster convergence and enhanced performance. In this work, we extend it with DINOv3 features, resulting in DEIMv2. DEIMv2 spans eight model sizes from X to Atto, covering GPU, edge, and mobile deployment. For the X, L, M, and S variants, we adopt DINOv3-pretrained / distilled backbones and introduce a Spatial Tuning Adapter (STA), which efficiently converts DINOv3's single-scale output into multi-scale features and complements strong semantics with fine-grained details to enhance detection. For ultra-lightweight models (Nano, Pico, Femto, and Atto), we employ HGNetv2 with depth and width pruning to meet strict resource budgets. Together with a simplified decoder and an upgraded Dense O2O, this unified design enables DEIMv2 to achieve a superior performance-cost trade-off across diverse scenarios, establishing new state-of-the-art results. Notably, our largest model, DEIMv2-X, achieves 57.8 AP with only 50.3M parameters, surpassing prior X-scale models that require over 60M parameters for just 56.5 AP. On the compact side, DEIMv2-S is the first sub-10M model (9.71M) to exceed the 50 AP milestone on COCO, reaching 50.9 AP. Even the ultra-lightweight DEIMv2-Pico, with just 1.5M parameters, delivers 38.5 AP-matching YOLOv10-Nano (2.3M) with ~50% fewer parameters.*
+
+## Usage
+
+```python
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
+from transformers.image_utils import load_image
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = load_image(url)
+
+image_processor = AutoImageProcessor.from_pretrained("harshaljanjani/DEIMv2_HGNetv2_N_COCO_Transformers")
+model = AutoModelForObjectDetection.from_pretrained("harshaljanjani/DEIMv2_HGNetv2_N_COCO_Transformers", device_map="auto")
+
+inputs = image_processor(images=image, return_tensors="pt").to(model.device)
+outputs = model(**inputs)
+
+results = image_processor.post_process_object_detection(
+    outputs, threshold=0.5, target_sizes=[image.size[::-1]]
+)
+
+for result in results:
+    for score, label, box in zip(result["scores"], result["labels"], result["boxes"]):
+        box = [round(i, 2) for i in box.tolist()]
+        print(f"Detected {model.config.id2label[label.item()]} with confidence {round(score.item(), 3)} at location {box}")
+```
+
+## Deimv2Config
+
+[[autodoc]] Deimv2Config
+
+## Deimv2Model
+
+[[autodoc]] Deimv2Model
+    - forward
+
+## Deimv2ForObjectDetection
+
+[[autodoc]] Deimv2ForObjectDetection
+    - forward

src/transformers/loss/loss_deimv2.py

@@ -0,0 +1,261 @@
+# 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.
+
+
+import torch
+import torch.nn.functional as F
+
+from ..utils import is_vision_available
+from .loss_d_fine import DFineLoss, _set_aux_loss, _set_aux_loss2
+from .loss_for_object_detection import box_iou
+
+
+if is_vision_available():
+    from transformers.image_transforms import center_to_corners_format
+
+
+class Deimv2Loss(DFineLoss):
+    def __init__(self, config):
+        super().__init__(config)
+        self.weight_dict = {
+            "loss_mal": config.weight_loss_mal,
+            "loss_bbox": config.weight_loss_bbox,
+            "loss_giou": config.weight_loss_giou,
+            "loss_fgl": config.weight_loss_fgl,
+            "loss_ddf": config.weight_loss_ddf,
+        }
+        self.losses = ["mal", "boxes", "local"]
+        self.mal_alpha = config.mal_alpha
+        self.use_dense_one_to_one = config.use_dense_one_to_one
+
+    def loss_labels_mal(self, outputs, targets, indices, num_boxes):
+        """Compute the Matching Aware Loss (MAL), which uses IoU-weighted soft labels
+        instead of hard one-hot targets, with focal-style weighting controlled by `mal_alpha`.
+        """
+        idx = self._get_source_permutation_idx(indices)
+
+        src_boxes = outputs["pred_boxes"][idx]
+        target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0)
+        ious, _ = box_iou(center_to_corners_format(src_boxes), center_to_corners_format(target_boxes))
+        ious = torch.diag(ious).detach()
+
+        src_logits = outputs["logits"]
+        target_classes_original = torch.cat([t["class_labels"][i] for t, (_, i) in zip(targets, indices)])
+        target_classes = torch.full(
+            src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device
+        )
+        target_classes[idx] = target_classes_original
+        target = F.one_hot(target_classes, num_classes=self.num_classes + 1)[..., :-1]
+
+        target_score_original = torch.zeros_like(target_classes, dtype=src_logits.dtype)
+        target_score_original[idx] = ious.to(target_score_original.dtype)
+        target_score = target_score_original.unsqueeze(-1) * target
+
+        pred_score = F.sigmoid(src_logits).detach()
+        target_score = target_score.pow(self.gamma)
+        if self.mal_alpha is not None:
+            weight = self.mal_alpha * pred_score.pow(self.gamma) * (1 - target) + target
+        else:
+            weight = pred_score.pow(self.gamma) * (1 - target) + target
+
+        loss = F.binary_cross_entropy_with_logits(src_logits, target_score, weight=weight, reduction="none")
+        loss = loss.mean(1).sum() * src_logits.shape[1] / num_boxes
+        return {"loss_mal": loss}
+
+    def _get_dense_o2o_indices(self, indices, indices_aux_list):
+        results = []
+        for indices_aux in indices_aux_list:
+            indices = [
+                (torch.cat([idx1[0], idx2[0]]), torch.cat([idx1[1], idx2[1]]))
+                for idx1, idx2 in zip(indices.copy(), indices_aux.copy())
+            ]
+
+        for index in [torch.cat([idx[0][:, None], idx[1][:, None]], 1) for idx in indices]:
+            unique, counts = torch.unique(index, return_counts=True, dim=0)
+            count_sort_indices = torch.argsort(counts, descending=True)
+            unique_sorted = unique[count_sort_indices]
+            column_to_row = {}
+            for idx_pair in unique_sorted:
+                row_idx, col_idx = idx_pair[0].item(), idx_pair[1].item()
+                if row_idx not in column_to_row:
+                    column_to_row[row_idx] = col_idx
+            final_rows = torch.tensor(list(column_to_row.keys()), device=index.device)
+            final_cols = torch.tensor(list(column_to_row.values()), device=index.device)
+            results.append((final_rows.long(), final_cols.long()))
+        return results
+
+    def get_loss(self, loss, outputs, targets, indices, num_boxes):
+        loss_map = {
+            "cardinality": self.loss_cardinality,
+            "local": self.loss_local,
+            "boxes": self.loss_boxes,
+            "focal": self.loss_labels_focal,
+            "vfl": self.loss_labels_vfl,
+            "mal": self.loss_labels_mal,
+        }
+        if loss not in loss_map:
+            raise ValueError(f"Loss {loss} not supported")
+        return loss_map[loss](outputs, targets, indices, num_boxes)
+
+    def forward(self, outputs, targets):
+        """
+        This performs the loss computation.
+
+        Args:
+             outputs (`dict`, *optional*):
+                Dictionary of tensors, see the output specification of the model for the format.
+             targets (`list[dict]`, *optional*):
+                List of dicts, such that `len(targets) == batch_size`. The expected keys in each dict depends on the
+                losses applied, see each loss' doc.
+        """
+        if not self.use_dense_one_to_one:
+            return super().forward(outputs, targets)
+
+        # Retrieve the matching between the outputs of the last layer and the targets
+        outputs_without_aux = {k: v for k, v in outputs.items() if "auxiliary_outputs" not in k}
+        indices = self.matcher(outputs_without_aux, targets)
+
+        # Compute the average number of target boxes across all nodes, for normalization purposes
+        num_boxes = sum(len(t["class_labels"]) for t in targets)
+        num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)
+        num_boxes = torch.clamp(num_boxes, min=1).item()
+
+        # Handle auxiliary outputs matching
+        cached_indices = []
+        indices_aux_list = []
+        if "auxiliary_outputs" in outputs:
+            for auxiliary_outputs in outputs["auxiliary_outputs"]:
+                aux_indices = self.matcher(auxiliary_outputs, targets)
+                cached_indices.append(aux_indices)
+                indices_aux_list.append(aux_indices)
+
+        # Dense one-to-one matching
+        indices_go = self._get_dense_o2o_indices(indices, indices_aux_list)
+        num_boxes_go = sum(len(x[0]) for x in indices_go)
+        num_boxes_go = torch.as_tensor([num_boxes_go], dtype=torch.float, device=next(iter(outputs.values())).device)
+        num_boxes_go = torch.clamp(num_boxes_go, min=1).item()
+
+        # Compute all the requested losses
+        losses = {}
+        for loss in self.losses:
+            use_union = loss in ("boxes", "local")
+            indices_in = indices_go if use_union else indices
+            num_boxes_in = num_boxes_go if use_union else num_boxes
+            l_dict = self.get_loss(loss, outputs, targets, indices_in, num_boxes_in)
+            l_dict = {k: l_dict[k] * self.weight_dict[k] for k in l_dict if k in self.weight_dict}
+            losses.update(l_dict)
+
+        # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
+        if "auxiliary_outputs" in outputs:
+            for i, auxiliary_outputs in enumerate(outputs["auxiliary_outputs"]):
+                for loss in self.losses:
+                    use_union = loss in ("boxes", "local")
+                    indices_in = indices_go if use_union else cached_indices[i]
+                    num_boxes_in = num_boxes_go if use_union else num_boxes
+                    l_dict = self.get_loss(loss, auxiliary_outputs, targets, indices_in, num_boxes_in)
+                    l_dict = {k: l_dict[k] * self.weight_dict[k] for k in l_dict if k in self.weight_dict}
+                    l_dict = {k + f"_aux_{i}": v for k, v in l_dict.items()}
+                    losses.update(l_dict)
+
+        # In case of cdn auxiliary losses. For deimv2
+        if "dn_auxiliary_outputs" in outputs:
+            if "denoising_meta_values" not in outputs:
+                raise ValueError(
+                    "The output must have the 'denoising_meta_values` key. "
+                    "Please, ensure that 'outputs' includes a 'denoising_meta_values' entry."
+                )
+            dn_indices = self.get_cdn_matched_indices(outputs["denoising_meta_values"], targets)
+            dn_num_boxes = num_boxes * outputs["denoising_meta_values"]["dn_num_group"]
+            for i, auxiliary_outputs in enumerate(outputs["dn_auxiliary_outputs"]):
+                for loss in self.losses:
+                    l_dict = self.get_loss(loss, auxiliary_outputs, targets, dn_indices, dn_num_boxes)
+                    l_dict = {k: l_dict[k] * self.weight_dict[k] for k in l_dict if k in self.weight_dict}
+                    l_dict = {k + f"_dn_{i}": v for k, v in l_dict.items()}
+                    losses.update(l_dict)
+
+        return losses
+
+
+def Deimv2ForObjectDetectionLoss(
+    logits,
+    labels,
+    device,
+    pred_boxes,
+    config,
+    outputs_class=None,
+    outputs_coord=None,
+    enc_topk_logits=None,
+    enc_topk_bboxes=None,
+    denoising_meta_values=None,
+    predicted_corners=None,
+    initial_reference_points=None,
+    **kwargs,
+):
+    criterion = Deimv2Loss(config)
+    criterion.to(device)
+
+    outputs_loss = {"logits": logits, "pred_boxes": pred_boxes.clamp(min=0, max=1)}
+    auxiliary_outputs = None
+
+    if config.auxiliary_loss:
+        if denoising_meta_values is not None:
+            dn_out_coord, normal_out_coord = torch.split(
+                outputs_coord.clamp(min=0, max=1), denoising_meta_values["dn_num_split"], dim=2
+            )
+            dn_out_class, normal_out_class = torch.split(outputs_class, denoising_meta_values["dn_num_split"], dim=2)
+            # https://github.com/Intellindust-AI-Lab/DEIMv2/blob/main/engine/deim/deim_decoder.py#L562-L571
+            # The original splits denoising queries in the decoder; here it happens in the loss since the decoder returns unsplit tensors.
+            _, normal_logits = torch.split(logits, denoising_meta_values["dn_num_split"], dim=1)
+            _, normal_pred_boxes = torch.split(pred_boxes, denoising_meta_values["dn_num_split"], dim=1)
+            dn_out_corners, out_corners = torch.split(predicted_corners, denoising_meta_values["dn_num_split"], dim=2)
+            dn_out_refs, out_refs = torch.split(initial_reference_points, denoising_meta_values["dn_num_split"], dim=2)
+
+            outputs_loss["logits"] = normal_logits
+            outputs_loss["pred_boxes"] = normal_pred_boxes.clamp(min=0, max=1)
+        else:
+            normal_out_coord = outputs_coord.clamp(min=0, max=1)
+            normal_out_class = outputs_class
+            out_corners = predicted_corners
+            out_refs = initial_reference_points
+
+        auxiliary_outputs = _set_aux_loss2(
+            normal_out_class[:, :-1].transpose(0, 1),
+            normal_out_coord[:, :-1].transpose(0, 1),
+            out_corners[:, :-1].transpose(0, 1),
+            out_refs[:, :-1].transpose(0, 1),
+            out_corners[:, -1],
+            normal_out_class[:, -1],
+        )
+
+        outputs_loss["auxiliary_outputs"] = auxiliary_outputs
+        outputs_loss["auxiliary_outputs"].extend(
+            _set_aux_loss([enc_topk_logits], [enc_topk_bboxes.clamp(min=0, max=1)])
+        )
+
+        if denoising_meta_values is not None:
+            dn_auxiliary_outputs = _set_aux_loss2(
+                dn_out_class.transpose(0, 1),
+                dn_out_coord.transpose(0, 1),
+                dn_out_corners.transpose(0, 1),
+                dn_out_refs.transpose(0, 1),
+                dn_out_corners[:, -1],
+                dn_out_class[:, -1],
+            )
+            outputs_loss["dn_auxiliary_outputs"] = dn_auxiliary_outputs
+            outputs_loss["denoising_meta_values"] = denoising_meta_values
+
+    loss_dict = criterion(outputs_loss, labels)
+
+    loss = sum(loss_dict.values())
+    return loss, loss_dict, auxiliary_outputs

src/transformers/loss/loss_utils.py

@@ -19,6 +19,7 @@
 
 from .loss_d_fine import DFineForObjectDetectionLoss
 from .loss_deformable_detr import DeformableDetrForObjectDetectionLoss, DeformableDetrForSegmentationLoss
+from .loss_deimv2 import Deimv2ForObjectDetectionLoss
 from .loss_for_object_detection import ForObjectDetectionLoss, ForSegmentationLoss
 from .loss_grounding_dino import GroundingDinoForObjectDetectionLoss
 from .loss_lw_detr import LwDetrForObjectDetectionLoss
@@ -163,6 +164,7 @@ def ForTokenClassification(logits: torch.Tensor, labels, config, **kwargs):
     "RTDetrForObjectDetection": RTDetrForObjectDetectionLoss,
     "RTDetrV2ForObjectDetection": RTDetrForObjectDetectionLoss,
     "DFineForObjectDetection": DFineForObjectDetectionLoss,
+    "Deimv2ForObjectDetection": Deimv2ForObjectDetectionLoss,
     "CsmForConditionalGeneration": ForCausalLMLoss,
     "LwDetrForObjectDetection": LwDetrForObjectDetectionLoss,
 }

src/transformers/models/__init__.py

@@ -96,6 +96,7 @@
     from .deepseek_vl import *
     from .deepseek_vl_hybrid import *
     from .deformable_detr import *
+    from .deimv2 import *
     from .deit import *
     from .deprecated import *
     from .depth_anything import *

src/transformers/models/auto/auto_mappings.py

@@ -125,6 +125,7 @@
         ("deepseek_vl", "DeepseekVLConfig"),
         ("deepseek_vl_hybrid", "DeepseekVLHybridConfig"),
         ("deformable_detr", "DeformableDetrConfig"),
+        ("deimv2", "Deimv2Config"),
         ("deit", "DeiTConfig"),
         ("depth_anything", "DepthAnythingConfig"),
         ("depth_pro", "DepthProConfig"),

src/transformers/models/auto/image_processing_auto.py

@@ -76,6 +76,7 @@
             ("convnextv2", {"torchvision": "ConvNextImageProcessor", "pil": "ConvNextImageProcessorPil"}),
             ("cvt", {"torchvision": "ConvNextImageProcessor", "pil": "ConvNextImageProcessorPil"}),
             ("data2vec-vision", {"torchvision": "BeitImageProcessor", "pil": "BeitImageProcessorPil"}),
+            ("deimv2", {"torchvision": "RTDetrImageProcessor", "pil": "RTDetrImageProcessorPil"}),
             ("depth_anything", {"torchvision": "DPTImageProcessor", "pil": "DPTImageProcessorPil"}),
             ("dinat", {"torchvision": "ViTImageProcessor", "pil": "ViTImageProcessorPil"}),
             ("dinov2", {"torchvision": "BitImageProcessor", "pil": "BitImageProcessorPil"}),

src/transformers/models/auto/modeling_auto.py

@@ -116,6 +116,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("deepseek_vl", "DeepseekVLModel"),
         ("deepseek_vl_hybrid", "DeepseekVLHybridModel"),
         ("deformable_detr", "DeformableDetrModel"),
+        ("deimv2", "Deimv2Model"),
         ("deit", "DeiTModel"),
         ("depth_pro", "DepthProModel"),
         ("detr", "DetrModel"),
@@ -1113,6 +1114,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("d_fine", "DFineForObjectDetection"),
         ("dab-detr", "DabDetrForObjectDetection"),
         ("deformable_detr", "DeformableDetrForObjectDetection"),
+        ("deimv2", "Deimv2ForObjectDetection"),
         ("detr", "DetrForObjectDetection"),
         ("lw_detr", "LwDetrForObjectDetection"),
         ("pp_doclayout_v2", "PPDocLayoutV2ForObjectDetection"),