Update README.md

README.md

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 ---
 library_name: transformers
----
+---
+
+This is the HF transformers implementation for RT-DETRv2
+
+Model: RT-DETRv2-S
+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. 
+
+Usage:
+
+```python
+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}")