orange_detetion_model

-------------------------HOW TO USE---------------------

from transformers import AutoProcessor, AutoModelForObjectDetection from PIL import Image import torch import matplotlib.pyplot as plt

COLORS = [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125], [0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933]] def plot_results(pil_img, scores, labels, boxes): plt.figure(figsize=(16,10)) plt.imshow(pil_img) ax = plt.gca() colors = COLORS * 100 for score, label, (xmin, ymin, xmax, ymax),c in zip(scores.tolist(), labels.tolist(), boxes.tolist(), colors): ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=c, linewidth=3)) text = f'{model.config.id2label[label]}: {score:0.2f}' ax.text(xmin, ymin, text, fontsize=15, bbox=dict(facecolor='yellow', alpha=0.5)) plt.axis('off') plt.show()

processor = AutoProcessor.from_pretrained('Lam-Hung/orange_detetion_model') model = AutoModelForObjectDetection.from_pretrained('Lam-Hung/orange_detetion_model').to('cuda')

image = Image.open('/content/C59_59.jpeg') inputs = processor(image, return_tensors="pt").to('cuda') with torch.no_grad(): outputs = model(**inputs) results = processor.post_process_object_detection(outputs, threshold=0.3, target_sizes=[image.size[::-1]])[0] plot_results(image, results['scores'], results['labels'], results['boxes'])