library_name: transformers

这是RT-DETRv2的HF transformers实现

模型：RT-DETRv2-S
RT-DETRv2是改进版实时检测变换器（RT-DETR）。该模型基于先前最先进的实时检测器RT-DETR，通过引入一组即插即用的优化方案提升灵活性与实用性，并优化训练策略以实现更强性能。为增强灵活性，我们建议在可变形注意力机制中为不同尺度的特征设置差异化采样点数量，使解码器能够实现选择性多尺度特征提取。

使用方法：

import torch
import requests

from PIL import Image
from transformers import RTDetrForObjectDetection, RTDetrImageProcessor

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

image_processor = RTDetrImageProcessor.from_pretrained("jadechoghari/RT-DETRv2")
model = RTDetrForObjectDetection.from_pretrained("jadechoghari/RT-DETRv2")

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.size[::-1]]), threshold=0.3)

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}")