| --- |
| license: mit |
| language: en |
| tags: |
| - pytorch |
| - image-classification |
| - computer-vision |
| - pothole-detection |
| --- |
| |
| # 🧠 Pothole Detection Model (ResNet18) |
|
|
| This repository contains a fine-tuned **ResNet18** model for classifying whether a road image contains a **pothole** or not. |
| The model was developed as part of the **Zindi MIIA Pothole Image Classification Challenge**, focused on improving road safety in South Africa. |
|
|
| ## 📚 Project Overview |
| - **Task:** Binary image classification (`pothole` / `no_pothole`) |
| - **Framework:** PyTorch + torchvision |
| - **Model:** ResNet18 (pretrained on ImageNet, fine-tuned) |
| - **Evaluation Metric:** AUC (Area Under the Curve) |
| - **Dataset Source:** [rupesh002/Patholes_Dataset](https://huggingface.co/datasets/rupesh002/Patholes_Dataset) |
|
|
| --- |
|
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| ## 🔗 Full Project Code |
| The complete project (training, inference, and notebook) is available on GitHub: |
| 👉 [MIIA Pothole Image Classification on GitHub](https://github.com/Rupeshbhardwaj002/MIIA_Pothole_Image_Classification_SouthAfrica) |
|
|
| It includes: |
| - Data preprocessing |
| - Model training pipeline (`train.py`) |
| - Prediction and submission script (`predict.py`) |
| - Notebook used for Zindi competition submission |
|
|
| --- |
|
|
| ## ⚙️ How to Load This Model |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| import joblib |
| |
| model_path = hf_hub_download( |
| repo_id="rupesh002/pothole_detection_model", |
| filename="model.pkl", |
| repo_type="model" |
| ) |
| |
| model = joblib.load(model_path) |
| --- |
| 🏷️ Author |
| |
| Rupesh Bhardwaj |
| Machine Learning Enthusiast |
