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# RT-DETRv2

<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>

## 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
