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# Example script for using the yolov8_l model from Hugging Face
import torch
import cv2
import numpy as np
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForObjectDetection
# Load model and processor
model = AutoModelForObjectDetection.from_pretrained("lkk688/yolov8l-model")
processor = AutoImageProcessor.from_pretrained("lkk688/yolov8l-model")
# Function to run inference on an image
def detect_objects(image_path, confidence_threshold=0.25):
# Load image
image = Image.open(image_path).convert("RGB")
# Process image
inputs = processor(images=image, return_tensors="pt")
# Run inference
with torch.no_grad():
outputs = model(**inputs)
# Post-process outputs
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(
outputs,
threshold=confidence_threshold,
target_sizes=target_sizes
)[0]
# Print results
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 "
f"{round(score.item(), 3)} at location {box}"
)
return results
# Example usage
if __name__ == "__main__":
# Replace with your image path
image_path = "path/to/your/image.jpg"
detect_objects(image_path)
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