detección de huevos de mosquito

import numpy as np
from PIL import ImageDraw

import torch
from datasets import load_dataset
from transformers import AutoImageProcessor, AutoModelForObjectDetection

TOKEN = 'genera tu token en https://huggingface.co/settings/tokens'

path_space = 'henryzord/mosquito-egg-detection'

image_processor = AutoImageProcessor.from_pretrained(path_space, token=TOKEN)
model = AutoModelForObjectDetection.from_pretrained(path_space, token=TOKEN)
dataset = load_dataset(path_space, token=TOKEN)
image = dataset['test'][np.random.randint(len(dataset['test']))]['image']

with torch.no_grad():
    inputs = image_processor(images=[image], return_tensors='pt')
    outputs = model(**inputs)
    target_sizes = torch.tensor([[image.size[1], image.size[0]]])
    results = image_processor.post_process_object_detection(outputs, threshold=0.3, 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"Detected {model.config.id2label[label.item()]} with confidence {round(score.item(), 3)} at location {box}")

    draw = ImageDraw.Draw(image)

    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
        box = [round(i, 2) for i in box.tolist()]
        x, y, x2, y2 = tuple(box)
        draw.rectangle((x, y, x2, y2), outline="red", width=1)
        draw.text((x, y2 + 2), model.config.id2label[label.item()], fill='red')

    image.show()