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*This model was released on 2021-08-13 and added to Hugging Face Transformers on 2022-09-22 and contributed by [DepuMeng](https://huggingface.co/DepuMeng).*

# Conditional DETR

[Conditional DETR](https://huggingface.co/papers/2108.06152) addresses slow training convergence in DETR by introducing a conditional cross-attention mechanism. This mechanism allows the decoder to learn a conditional spatial query, enabling each cross-attention head to focus on distinct regions such as object extremities or internal regions. This approach reduces reliance on high-quality content embeddings, simplifying training and achieving up to 10× faster convergence for stronger backbones.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="object-detection", model="microsoft/conditional-detr-resnet-50", dtype="auto")
pipeline("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
import requests
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForObjectDetection

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = AutoImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50")
model = AutoModelForObjectDetection.from_pretrained("microsoft/conditional-detr-resnet-50", dtype="auto")

inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
target_sizes = torch.tensor([image.size[::-1]])
results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[
    0
]
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
    box = [round(i, 2) for i in box.tolist()]
    print(
        f"Detected {model.config.id2label[label.item()]} with confidence "
        f"{round(score.item(), 3)} at location {box}"
    )
```

</hfoption>
</hfoptions>

## ConditionalDetrConfig

[[autodoc]] ConditionalDetrConfig

## ConditionalDetrImageProcessor

[[autodoc]] ConditionalDetrImageProcessor
    - preprocess
    - post_process_object_detection
    - post_process_instance_segmentation
    - post_process_semantic_segmentation
    - post_process_panoptic_segmentation

## ConditionalDetrImageProcessorFast

[[autodoc]] ConditionalDetrImageProcessorFast
    - preprocess
    - post_process_object_detection
    - post_process_instance_segmentation
    - post_process_semantic_segmentation
    - post_process_panoptic_segmentation

## ConditionalDetrModel

[[autodoc]] ConditionalDetrModel
    - forward

## ConditionalDetrForObjectDetection

[[autodoc]] ConditionalDetrForObjectDetection
    - forward

## ConditionalDetrForSegmentation

[[autodoc]] ConditionalDetrForSegmentation
    - forward

