Add-Deimv2

docs/source/en/model_doc/deimv2.md

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+
+*This model was released in 2025 and added to Hugging Face Transformers in 2025-10.* [web:28][web:25]
+
+# DEIMv2
+
+<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">
+    <img alt="Object Detection" src="https://img.shields.io/badge/Object%20Detection-0ea5e9?style=flat">
+    <img alt="AutoBackbone" src="https://img.shields.io/badge/AutoBackbone-16a34a?style=flat">
+  </div>
+</div>
+
+## Overview
+
+DEIMv2 is a real‑time object detection architecture built on DINOv3 features, introducing a Spatial Tuning Adapter (STA) to convert single‑scale ViT features into a lightweight multi‑scale pyramid, a simplified decoder, and an upgraded Dense one‑to‑one matching strategy. [web:16][web:6]
+
+This integration uses the AutoBackbone API so DINO‑family backbones can be reused without re‑implementation in the detection head; the initial release targets DINOv3/ViT backbones, with tiny HGNetv2 variants planned as follow‑ups. [web:17][web:28]
+
+> [!TIP]
+> The smallest working example below shows how to run inference and obtain boxes, scores, and labels from post‑processing. [web:25][web:28]
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+from PIL import Image
+from transformers import pipeline
+
+detector = pipeline(
+task="object-detection",
+model="your-org/deimv2-dinov3-base"
+)
+image = Image.open("path/to/your/image.jpg")
+outputs = detector(image)
+print(outputs[:3])
+
+text
+[web:25][web:28]
+
+</hfoption>
+<hfoption id="AutoModel">
+
+from PIL import Image
+import requests
+from transformers import Deimv2ImageProcessor, Deimv2ForObjectDetection
+
+ckpt = "your-org/deimv2-dinov3-base" # replace when a checkpoint is available
+model = Deimv2ForObjectDetection.from_pretrained(ckpt)
+processor = Deimv2ImageProcessor.from_pretrained(ckpt)
+
+url = "https://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+inputs = processor.preprocess([image], return_tensors="pt")
+outputs = model(**inputs)
+results = processor.post_process_object_detection(outputs, threshold=0.5)
+print(results)
+
+text
+[web:25][web:28]
+
+</hfoption>
+<hfoption id="transformers CLI">
+
+echo -e "https://images.cocodataset.org/val2017/000000039769.jpg" | transformers run
+--task object-detection
+--model your-org/deimv2-dinov3-base
+
+text
+[web:25][web:28]
+
+</hfoption>
+</hfoptions>
+
+## Model notes
+
+- Backbone via AutoBackbone: loads DINOv3/ViT variants and exposes feature maps to the DEIMv2 head. [web:17][web:28]
+- Spatial Tuning Adapter: transforms single‑scale features into a multi‑scale pyramid for accurate localization with minimal overhead. [web:16][web:6]
+- Decoder and Dense O2O: streamlined decoder with one‑to‑one assignment for stable training and real‑time throughput. [web:16][web:6]
+
+## Expected inputs and outputs
+
+- Inputs: `pixel_values` shaped \(B \times 3 \times H \times W\), produced by `Deimv2ImageProcessor.preprocess`. [web:43][web:25]
+- Outputs: class `logits` \(B \times Q \times C\) and normalized `pred_boxes` \(B \times Q \times 4\); use `post_process_object_detection` to filter and convert to absolute coordinates. [web:43][web:28]
+
+## Configuration
+
+[[autodoc]] Deimv2Config
+  - init
+
+This configuration defines backbone settings, query count, decoder depth, and STA parameters, and sets `model_type="deimv2"`. [web:28][web:44]
+
+## Base model
+
+[[autodoc]] Deimv2Model
+  - forward
+
+This module wires the backbone to STA and the decoder, returning decoder hidden states for the detection head. [web:28][web:17]
+
+## Task head
+
+[[autodoc]] Deimv2ForObjectDetection
+  - forward
+
+This head predicts class logits and normalized bounding boxes for a fixed set of queries. [web:25][web:28]
+
+## Image Processor
+
+[[autodoc]] Deimv2ImageProcessor
+  - preprocess
+  - post_process_object_detection
+
+Handles resizing, normalization, batching, and conversion of model outputs to boxes, scores, and labels. [web:43][web:25]
+
+## Resources
+
+- Paper: “Real‑Time Object Detection Meets DINOv3.” [web:16][web:7]
+- Official repository and model zoo for reference implementations and weights. [web:3][web:12]
+- AutoBackbone documentation for reusing vision backbones. [web:17][web:28]
+
+## Citations
+
+Please cite the original DEIMv2 paper when using this model: “Real‑Time Object Detection Meets DINOv3.” [web:16][web:7]

src/transformers/models/deimv2/__init__.py

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+from .configuration_deimv2 import Deimv2Config
+from .image_processing_deimv2 import Deimv2ImageProcessor
+from .modeling_deimv2 import Deimv2Model, Deimv2ForObjectDetection
+
+__all__ = [
+    "Deimv2Config",
+    "Deimv2ImageProcessor",
+    "Deimv2Model",
+    "Deimv2ForObjectDetection",
+]
+

src/transformers/models/deimv2/configuration_deimv2.py

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+from dataclasses import dataclass
+from typing import Optional, Dict, Any
+from ..auto.configuration_auto import AutoBackboneConfig
+from ...configuration_utils import PretrainedConfig
+
+@dataclass
+class Deimv2Preset:
+    hidden_dim: int
+    num_queries: int
+    num_decoder_layers: int
+    backbone: str
+
+DEIMV2_PRESETS: Dict[str, Deimv2Preset] = {
+    "base-dinov3-s": Deimv2Preset(hidden_dim=256, num_queries=300, num_decoder_layers=6, backbone="facebook/dinov2-small"),
+    "base-dinov3-b": Deimv2Preset(hidden_dim=256, num_queries=300, num_decoder_layers=6, backbone="facebook/dinov2-base"),
+}
+
+class Deimv2Config(PretrainedConfig):
+    model_type = "deimv2"
+
+    def __init__(
+        self,
+        backbone_config: Optional[Dict[str, Any]] = None,
+        hidden_dim: int = 256,
+        num_queries: int = 300,
+        num_decoder_layers: int = 6,
+        num_labels: int = 91,
+        # STA and decoder knobs (placeholders)
+        sta_num_scales: int = 4,
+        use_dense_o2o: bool = True,
+        layer_norm_type: str = "rms",
+        activation: str = "swish",
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.backbone_config = backbone_config or AutoBackboneConfig.from_pretrained(DEIMV2_PRESETS["base-dinov3-b"].backbone).to_dict()
+        self.hidden_dim = hidden_dim
+        self.num_queries = num_queries
+        self.num_decoder_layers = num_decoder_layers
+        self.num_labels = num_labels
+        self.sta_num_scales = sta_num_scales
+        self.use_dense_o2o = use_dense_o2o
+        self.layer_norm_type = layer_norm_type
+        self.activation = activation
+    @classmethod
+    def from_preset(cls, preset_name: str, **kwargs) -> "Deimv2Config":
+        if preset_name not in DEIMV2_PRESETS:
+            raise ValueError(f"Preset '{preset_name}' not found. Available presets: {list(DEIMV2_PRESETS.keys())}")
+        preset = DEIMV2_PRESETS[preset_name]
+        backbone_config = AutoBackboneConfig.from_pretrained(preset.backbone).to_dict()
+        return cls(
+            backbone_config=backbone_config,
+            hidden_dim=preset.hidden_dim,
+            num_queries=preset.num_queries,
+            num_decoder_layers=preset.num_decoder_layers,
+            **kwargs,
+        )

src/transformers/models/deimv2/image_processing_deimv2.py

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+from typing import List, Dict, Any, Union
+import torch
+from PIL import Image
+from ...image_processing_utils import BaseImageProcessor, BatchFeature
+from ...image_transforms import resize, normalize, to_channel_dimension_format
+from ...utils.torch_utils import is_torch_tensor
+
+class Deimv2ImageProcessor(BaseImageProcessor):
+    model_input_names = ["pixel_values"]
+
+    def __init__(self, size: int = 1024, image_mean=None, image_std=None, **kwargs):
+        super().__init__(**kwargs)
+        self.size = size
+        self.image_mean = image_mean or [0.485, 0.456, 0.406]
+        self.image_std = image_std or [0.229, 0.224, 0.225]
+
+    def preprocess(self, images: List[Union[Image.Image, "np.ndarray", torch.Tensor]], return_tensors="pt", **kwargs) -> BatchFeature:
+        pixel_values = []
+        for img in images:
+            if not is_torch_tensor(img):
+                img = Image.fromarray(img) if not isinstance(img, Image.Image) else img
+                img = resize(img, size={"shortest_edge": self.size})
+                img = to_channel_dimension_format(img, "channels_first")
+                img = normalize(img, mean=self.image_mean, std=self.image_std)
+            pixel_values.append(torch.as_tensor(img, dtype=torch.float32))
+        pixel_values = torch.stack(pixel_values, dim=0)
+        return BatchFeature(data={"pixel_values": pixel_values}, tensor_type=return_tensors)
+
+    def post_process_object_detection(self, outputs, threshold: float = 0.5, target_sizes=None) -> List[Dict[str, Any]]:
+        # Minimal passthrough; replace with real box/logit decoding
+        logits = outputs["logits"]
+        boxes = outputs["pred_boxes"]
+        probs = logits.sigmoid()
+        results = []
+        for prob, box in zip(probs, boxes):
+            keep = prob.max(dim=-1).values > threshold
+            results.append({"scores": prob[keep].max(dim=-1).values, "labels": prob[keep].argmax(dim=-1), "boxes": box[keep]})
+        return results
+        if target_sizes is not None:
+            for result, size in zip(results, target_sizes):
+                img_h, img_w = size
+                boxes = result["boxes"]
+                boxes = boxes * torch.tensor([img_w, img_h, img_w, img_h], dtype=boxes.dtype, device=boxes.device)
+                result["boxes"] = boxes
+        return results

src/transformers/models/deimv2/modeling_deimv2.py

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+from typing import Optional, Tuple, Dict, Any
+import torch
+import torch.nn as nn
+from ...modeling_utils import PreTrainedModel
+from ..auto import AutoBackbone
+from .configuration_deimv2 import Deimv2Config
+from ...utils import logging
+
+logger = logging.get_logger(__name__)
+
+class Deimv2PreTrainedModel(PreTrainedModel):
+    config_class = Deimv2Config
+    base_model_prefix = "deimv2"
+    _no_split_modules = []
+
+class SpatialTuningAdapter(nn.Module):
+    def __init__(self, hidden_dim: int, num_scales: int):
+        super().__init__()
+        self.proj = nn.ModuleList([nn.Conv2d(hidden_dim, hidden_dim, 1) for _ in range(num_scales)])
+
+    def forward(self, feat: torch.Tensor) -> Tuple[torch.Tensor, ...]:
+        # feat: (B, C, H, W); create a toy pyramid by striding
+        feats = []
+        x = feat
+        for i, p in enumerate(self.proj):
+            feats.append(p(x))
+            if i < len(self.proj) - 1:
+                x = nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
+        return tuple(feats)
+
+class SimpleDecoder(nn.Module):
+    def __init__(self, hidden_dim: int, num_layers: int, num_queries: int):
+        super().__init__()
+        self.query_embed = nn.Embedding(num_queries, hidden_dim)
+        self.layers = nn.ModuleList([nn.TransformerDecoderLayer(d_model=hidden_dim, nhead=8, dim_feedforward=hidden_dim * 4, batch_first=True) for _ in range(num_layers)])
+        self.decoder = nn.TransformerDecoder(self.layers[0], num_layers=num_layers)
+
+    def forward(self, feats: Tuple[torch.Tensor, ...]) -> torch.Tensor:
+        # Use the highest-resolution feature for a stub attention target
+        bs = feats[0].size(0)
+        tgt = self.query_embed.weight.unsqueeze(0).expand(bs, -1, -1)
+        # Flatten spatial dims
+        f = feats[0].flatten(2).transpose(1, 2)  # (B, HW, C)
+        memory = f
+        hs = self.decoder(tgt, memory)  # (B, Q, C)
+        return hs
+
+class Deimv2Model(Deimv2PreTrainedModel):
+    def __init__(self, config: Deimv2Config):
+        super().__init__(config)
+        self.backbone = AutoBackbone.from_config(config.backbone_config)
+        out_channels = self.backbone.channels
+        hidden = config.hidden_dim
+        if isinstance(out_channels, (tuple, list)):
+            backbone_dim = out_channels[0]
+        else:
+            backbone_dim = out_channels
+        self.input_proj = nn.Conv2d(backbone_dim, hidden, kernel_size=1)
+        self.sta = SpatialTuningAdapter(hidden_dim=hidden, num_scales=config.sta_num_scales)
+        self.decoder = SimpleDecoder(hidden_dim=hidden, num_layers=config.num_decoder_layers, num_queries=config.num_queries)
+
+    def forward(self, pixel_values: torch.Tensor, return_dict: bool = True, **kwargs) -> Dict[str, torch.Tensor]:
+        features = self.backbone(pixel_values).feature_maps  # tuple of (B, C, H, W)
+        x = features[0]
+        x = self.input_proj(x)
+        feats = self.sta(x)
+        hs = self.decoder(feats)  # (B, Q, C)
+        return {"decoder_hidden_states": hs}
+
+class Deimv2ForObjectDetection(Deimv2PreTrainedModel):
+    def __init__(self, config: Deimv2Config):
+        super().__init__(config)
+        self.model = Deimv2Model(config)
+        hidden = config.hidden_dim
+        self.class_head = nn.Linear(hidden, config.num_labels)
+        self.box_head = nn.Linear(hidden, 4)
+
+    def forward(self, pixel_values: torch.Tensor, labels: Optional[Dict[str, torch.Tensor]] = None, **kwargs) -> Dict[str, torch.Tensor]:
+        outputs = self.model(pixel_values, return_dict=True)
+        hs = outputs["decoder_hidden_states"]
+        logits = self.class_head(hs)
+        boxes = self.box_head(hs).sigmoid()
+        out = {"logits": logits, "pred_boxes": boxes}
+        # TODO: compute loss if labels provided
+        return out
+
+    def freeze_backbone(self):
+        for param in self.model.backbone.parameters():
+            param.requires_grad = False
+        logger.info("Backbone frozen.")
+        self.model.backbone.eval()
+
+

tests/models/deimv2/test_configuration_deimv2.py

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+from transformers import Deimv2Config
+def test_roundtrip():
+    cfg = Deimv2Config()
+    s = cfg.to_json_string()
+    cfg2 = Deimv2Config.from_json_string(s)
+    assert cfg2.model_type == "deimv2"
+    assert cfg2.hidden_dim == cfg.hidden_dim

tests/models/deimv2/test_image_processing_deimv2.py

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+import torch
+from PIL import Image
+import numpy as np
+from transformers import Deimv2ImageProcessor
+
+def test_preprocess_postprocess():
+    proc = Deimv2ImageProcessor(size=256)
+    img = Image.fromarray((np.random.rand(256,256,3)*255).astype("uint8"))
+    batch = proc.preprocess([img])
+    assert "pixel_values" in batch
+    dummy = {"logits": torch.randn(1, 300, 91), "pred_boxes": torch.rand(1, 300, 4)}
+    res = proc.post_process_object_detection(dummy, threshold=0.9)
+    assert isinstance(res, list)
+    assert "scores" in res[0]

tests/models/deimv2/test_modeling_deimv2.py

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+import torch
+from transformers import Deimv2Config
+from transformers.models.deimv2.modeling_deimv2 import Deimv2ForObjectDetection
+
+def test_forward_shapes():
+    cfg = Deimv2Config()
+    model = Deimv2ForObjectDetection(cfg)
+    pixel_values = torch.randn(2, 3, 512, 512)
+    out = model(pixel_values)
+    assert out["logits"].shape[:2] == (2, cfg.num_queries)
+    assert out["pred_boxes"].shape[-1] == 4
+