Rtdetr V2 R34vd

🚀 RT-DETRv2

RT-DETRv2 is an improved object - detection model that enhances flexibility and practicality while maintaining real - time performance.

🚀 Quick Start

This section provides a brief introduction to the RT - DETRv2 model and how to use it for object detection.

✨ Features

• Enhanced Design: 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.
• Performance: It consistently outperforms its predecessor across all model sizes while maintaining the same real - time speeds.
• Versatility: Ideal for real - time object detection in diverse applications such as autonomous driving, surveillance systems, robotics, and retail analytics.

📚 Documentation

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. These changes enhance flexibility and practicality while maintaining real - time performance.

This model was contributed by @jadechoghari with the help of @cyrilvallez and [@qubvel - hf](https://huggingface.co/qubvel - hf)

Performance

RT - DETRv2 consistently outperforms its predecessor across all model sizes while maintaining the same real - time speeds.


Training

RT - DETRv2 is trained on COCO (Lin et al. [2014]) train2017 and validated on COCO val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 − 0.95 with a step size of 0.05), and APval50 commonly used in real scenarios.

Applications

RT - DETRv2 is ideal for real - time object detection in diverse applications such as autonomous driving, surveillance systems, robotics, and retail analytics. Its enhanced flexibility and deployment - friendly design make it suitable for both edge devices and large - scale systems + ensures high accuracy and speed in dynamic, real - world environments.

💻 Usage Examples

Basic Usage

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_r34vd")
model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r34vd")

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}")

Output Example

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]

📄 License

This project is licensed under the Apache - 2.0 license.