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TanTan2025/archivision-yolo
archivision-yolo is a object detection model from TanTan2025. Use it when you need objects located in an image. It is set up for ultralytics. The card lists the license as mit.
YOLOv8 model trained for architectural element detection and segmentation.
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From the Hugging Face model README
YOLOv8 model trained for architectural element detection and segmentation.
The model detects the following architectural elements:
window, door, column, arch, balcony, roof, facade, stairs, chimney, wall
from ultralytics import YOLO
# Load model
model = YOLO('best.pt')
# Run inference
results = model.predict('image.jpg')
# Process results
for result in results:
boxes = result.boxes
masks = result.masks
for box in boxes:
class_id = int(box.cls[0])
confidence = float(box.conf[0])
print(f"Detected: {model.names[class_id]} ({confidence:.2f})")
import requests
API_URL = "https://api-inference.huggingface.co/models/YOUR_USERNAME/archivision-yolo"
headers = {"Authorization": f"Bearer {YOUR_HF_TOKEN}"}
def query(image_url):
response = requests.post(API_URL, headers=headers, json={"inputs": image_url})
return response.json()
output = query("https://example.com/building.jpg")
print(output)
async function detectArchitecturalElements(imageUrl: string) {
const response = await fetch(
'https://api-inference.huggingface.co/models/YOUR_USERNAME/archivision-yolo',
{
method: 'POST',
headers: {
'Authorization': `Bearer ${process.env.HUGGINGFACE_TOKEN}`,
'Content-Type': 'application/json'
},
body: JSON.stringify({ inputs: imageUrl })
}
);
const predictions = await response.json();
return predictions;
}
This model was trained on architectural images with the following elements: window, door, column, arch, balcony, roof, facade, stairs, chimney, wall
MIT License
If you use this model, please cite:
@misc{archivision-yolo,
author = {ArchiVision Team},
title = {ArchiVision YOLO Model},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/YOUR_USERNAME/archivision-yolo}
}