transformers/docs/source/en/model_doc/rt_detr_v2.md

RT-DETRv2

Overview

The RT-DETRv2 model was proposed in RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer 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. The original code can be found here.

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

>>> 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.

• Scripts for finetuning [RTDetrV2ForObjectDetection] with [Trainer] or Accelerate can be found here.
• See also: Object detection task guide.
• Notebooks for inference and fine-tuning RT-DETRv2 on a custom dataset (🌎).

RTDetrV2Config

autodoc RTDetrV2Config

RTDetrV2Model

autodoc RTDetrV2Model - forward

RTDetrV2ForObjectDetection

autodoc RTDetrV2ForObjectDetection - forward