Object Detection
ultralytics
LiteRT
ONNX
yolo
yolo11
yolo11n
road-damage
pothole
sewage-manhole
mobile
ncnn
Instructions to use tahaUgan/pothole-yolo11n with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use tahaUgan/pothole-yolo11n with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("tahaUgan/pothole-yolo11n", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Pothole & Sewage-Manhole Detection (YOLO11n)
Trained YOLO11n model detecting Potholes and Sewage Manholes for road safety and infrastructure inspection.
π― Classes
0:Pothole1:Sewage-Manhole
π¦ Available Model Formats
| Format | File | Target Platform |
|---|---|---|
| PyTorch | pothole2v.pt |
Python / Ultralytics / Server |
| NCNN | pothole2v_ncnn_model.zip |
Android C++ / Vulkan GPU (Galaxy A16 / Mobile) |
| ONNX | pothole2v_320.onnx |
WebAssembly / WebGPU / Browser Camera |
| TFLite | pothole2v.tflite |
Android Kotlin / Java Apps |
β‘ Direct Download Links
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