detr

# Para usar este modelo:
from transformers import DetrForObjectDetection, DetrImageProcessor
import torch
from PIL import Image
import requests

model = DetrForObjectDetection.from_pretrained('Yaroslava270602/detr')
processor = DetrImageProcessor(model.config)

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

# Preprocesar la imagen
inputs = processor(images=image, return_tensors="pt")

# Realizar inferencia
outputs = model(**inputs)

# Obtener cajas de detección de objetos
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes)[0]

for score, label, box in zip(results['scores'], results['labels'], results['boxes']):
    box = [round(i, 2) for i in box.tolist()]
    print(f'Etiqueta: {model.config.id2label[label.item()]} | Puntuación: {round(score.item(), 3)} | Caja: {box}')