metadata
license: apache-2.0
base_model:
- Ultralytics/YOLO26
tags:
- icicle
- roof
- facade
- building
metrics:
- precision = 0.6
- recall = 0.52
- mAP50 = 0.52
- mAP50-95 = 0.22
🧊 Icicle Detector – YOLO26x model
This model is trained for automatic detection of icicles on images of building roofs and facades.
It detects one class – icicle – and outputs bounding boxes around each detected icicle.
Architecture: YOLO26x (custom modification based on YOLO).
Weights file: model.pt
Inference example
from ultralytics import YOLO
import cv2
# Load the model from Hugging Face
model = YOLO("IgorKir16/icicle-detector/model.pt")
# Run detection on an image
results = model("path/to/your_image.jpg", conf=0.25)
# Visualize results
for r in results:
im_array = r.plot()
cv2.imshow("Result", im_array)
cv2.waitKey(0)
# Print bounding boxes and confidence
for r in results:
for box in r.boxes:
x1, y1, x2, y2 = box.xyxy[0].tolist()
conf = box.conf[0].item()
cls = int(box.cls[0].item())
print(f"icicle: ({int(x1)}, {int(y1)}) – ({int(x2)}, {int(y2)}), confidence: {conf:.2f}")