PekingU
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rtdetr_v2_r101vd

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 library_name: transformers
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 ---
 library_name: transformers
+pipeline_tag: object-detection
 ---
+## RT-DETRv2
+### **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.
+This model was contributed by [@jadechoghari](https://x.com/jadechoghari) with the help of [@cyrilvallez](https://huggingface.co/cyrilvallez) and [@qubvel-hf](https://huggingface.co/qubvel-hf)
+This is
+### **Performance**
+RT-DETRv2 consistently outperforms its predecessor across all model sizes while maintaining the same real-time speeds.
+![rt-detr-v2-graph.png](https://huggingface.co/datasets/jadechoghari/images/resolve/main/rt-detr-v2-graph.png)
+### **How to use**
+```python
+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("jadechoghari/rtdetr_v2_r18vd")
+model = RTDetrV2ForObjectDetection.from_pretrained("jadechoghari/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]
+```
+### **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.