yolos-small-Axial_MRIs

Para obtener más información sobre cómo se creó, consulte el siguiente enlace: yolos-small-Axial_MRIs

Ejemplo de uso

from transformers import YolosFeatureExtractor, YolosForObjectDetection
from PIL import Image
import requests
import torch

url = 'https://path/to/your/image.jpg'
image = Image.open(requests.get(url, stream=True).raw)

feature_extractor = YolosFeatureExtractor.from_pretrained('DunnBC22/yolos-small-Axial_MRIs')
model = YolosForObjectDetection.from_pretrained('DunnBC22/yolos-small-Axial_MRIs')

inputs = feature_extractor(images=image, return_tensors='pt')
outputs = model(**inputs)

# let's only keep detections with score > 0.9 (You can change this threshold)
target_sizes = torch.tensor([image.size[::-1]])
results = feature_extractor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
boxes, scores, labels = results['boxes'], results['scores'], results['labels']

for box, score, label in zip(boxes, scores, labels):
    box = [round(i, 2) for i in box.tolist()]
    print(f'detected {model.config.id2label[label.item()]} with confidence {round(score.item(), 3)} at location {box}')