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MODEL_CARD.md

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-# 🧾 Model Card — PotholeNet-YOLO11m-v1
-
-## 🧠 Model Overview
-
-**PotholeNet-YOLO11m-v1** is a fine-tuned object detection model built on **Ultralytics YOLO11m** architecture, specifically trained to detect potholes, road damage, and garbage from street-level imagery. The model leverages YOLO11m's C2PSA (Cross-Stage Partial with Spatial Attention) mechanism, making it highly effective at identifying irregular-shaped urban defects like potholes.
-
-Trained on a large-scale, curated civic infrastructure dataset of **23,000+ street-level images** from Indian urban environments, this model is designed to power real-time civic issue detection systems, enabling automated reporting and faster municipal response.
-
-It serves as the **Detection Layer (Layer 1)** of the **Aamchi City AI Civic System** — an end-to-end intelligent dashboard for urban infrastructure monitoring.
-
----
-
-## 🏗️ Training Details
-
-| Parameter | Value |
-|:---|:---|
-| **Base Model** | `yolo11m.pt` (COCO pretrained) |
-| **Architecture** | YOLO11m (C3k2 + C2PSA Spatial Attention) |
-| **Framework** | Ultralytics v8.x |
-| **Training Hardware** | Kaggle — NVIDIA T4 ×2 (Dual GPU) |
-| **Epochs** | 50 |
-| **Input Resolution** | 768×768 |
-| **Batch Size** | Auto (`batch=-1`) |
-| **Optimizer** | AdamW |
-| **Learning Rate** | `lr0=0.001`, cosine decay to `lrf=0.01` |
-| **Warmup** | 3 epochs |
-| **Weight Decay** | 0.0005 |
-| **AMP** | Enabled (FP16 mixed precision) |
-| **Early Stopping** | `patience=10` (did not trigger — model was still improving) |
-
-### Loss Weights
-| Loss | Weight |
-|:---|:---|
-| Box Loss | 7.5 |
-| Classification Loss | 1.0 |
-| DFL Loss | 1.5 |
-
-### Augmentation Pipeline
-| Augmentation | Value |
-|:---|:---|
-| Mosaic | 1.0 |
-| MixUp | 0.15 |
-| Copy-Paste | 0.1 |
-| HSV (H/S/V) | 0.015 / 0.7 / 0.4 |
-| Rotation | ±10° |
-| Scale | 0.5 |
-| Shear | 2.0 |
-| Horizontal Flip | 0.5 |
-| Erasing | 0.3 |
-| Label Smoothing | 0.05 |
-| Close Mosaic | Last 8 epochs |
-
----
-
-## 📊 Dataset Description
-
-The model was trained on a curated subset of **23,179 street-level images** collected from Indian urban environments. The dataset underwent extensive preprocessing:
-
-- **Perceptual Hash (pHash) Deduplication** — Removed near-duplicate images using hamming distance ≤ 4
-- **Corrupt Image Removal** — Verified all images via PIL
-- **Intelligent Negative Sampling** — Trimmed empty-label (background) images to 2,000 hard negatives
-- **Stratified Split** — 80% Train / 15% Val / 5% Test, stratified by dominant class
-
-### Label Classes
-
-| Class ID | Class Name | Description |
-|:---|:---|:---|
-| 🔴 0 | **Pothole** | Road surface cavities and depressions |
-| 🟡 1 | **Road Damage** | Cracks, surface wear, and structural deterioration |
-| 🟢 2 | **Garbage** | Street-level waste and debris accumulation |
-
-> **Priority:** Pothole (primary) > Garbage > Road Damage
-
----
-
-## 🎯 Evaluation Metrics
-
-| Metric | Score |
-|:---|:---|
-| **mAP50** | **0.60** |
-| **mAP50-95** | — |
-| **Parameters** | ~20M |
-| **Model Size** | ~39 MB |
-| **Inference Speed** | Real-time on GPU |
-
-> ⚡ The model did not trigger early stopping at 50 epochs, indicating further training could yield additional performance gains.
-
----
-
-## 💬 Example Usage
-
-### Python (Ultralytics)
-
-```python
-from ultralytics import YOLO
-
-# Load model
-model = YOLO("best.pt")
-
-# Run inference
-results = model("street_image.jpg", imgsz=768, conf=0.25)
-
-# Display results
-results[0].show()
-
-# Access detections
-for box in results[0].boxes:
-    cls = int(box.cls)
-    conf = float(box.conf)
-    xyxy = box.xyxy[0].tolist()
-    class_names = {0: "pothole", 1: "road_damage", 2: "garbage"}
-    print(f"{class_names[cls]}: {conf:.2f} at {xyxy}")
-```
-
-### With Test-Time Augmentation (TTA)
-
-```python
-# TTA boosts mAP by +1-3% at the cost of inference speed
-results = model("street_image.jpg", imgsz=768, conf=0.25, augment=True)
-```
-
-### Filter Pothole-Only Detections
-
-```python
-results = model("street_image.jpg", conf=0.25)
-boxes = results[0].boxes
-pothole_mask = boxes.cls == 0
-pothole_boxes = boxes[pothole_mask]
-print(f"Found {len(pothole_boxes)} potholes")
-```
-
----
-
-## 🧩 Intended Use
-
-- **Real-time pothole detection** from dashcam, mobile phone, or street-view imagery
-- **Automated civic issue reporting** — GPS-tagged detection for municipal dashboards
-- **Infrastructure health monitoring** — Severity scoring and trend analysis for road maintenance
-- **Smart city integration** — Layer 1 detection input for AI-driven civic action systems
-- **Mobile deployment** — Exportable to ONNX for edge inference on mobile devices
-
----
-
-## ⚠️ Limitations
-
-- The model is optimized for **Indian urban road conditions**; performance may degrade on highways, rural roads, or non-Indian geographies.
-- **Road damage** class has visual overlap with potholes, which may cause occasional misclassification between the two.
-- Performance is best on **daytime, clear-weather imagery** — low-light and rain-occluded scenes may reduce accuracy.
-- The model was trained for **50 epochs without early stopping trigger**, suggesting the checkpoint is not fully converged and further fine-tuning could improve results.
-- **Small potholes** (< 32px at 768px resolution) may be missed in wide-angle shots.
-
----
-
-## 🧑‍💻 Developer
-
-| | |
-|:---|:---|
-| **Author** | Vansh Momaya |
-| **Institution** | D. J. Sanghvi College of Engineering |
-| **Focus Area** | Computer Vision, Object Detection, AI for Civic Infrastructure |
-| **Email** | vanshmomaya9@gmail.com |
-
----
-
-## 🌍 Citation
-
-If you use PotholeNet-YOLO11m-v1 in your research or project:
-
-```bibtex
-@online{momaya2026potholenet,
-  author       = {Vansh Momaya},
-  title        = {PotholeNet-YOLO11m-v1: Real-Time Pothole and Civic Issue Detection for Indian Urban Roads},
-  year         = {2026},
-  version      = {v1},
-  url          = {https://huggingface.co/Vansh180/PotholeNet-YOLO11m-v1},
-  institution  = {D. J. Sanghvi College of Engineering},
-  note         = {Fine-tuned YOLO11m model for detecting potholes, road damage, and garbage in Indian street imagery},
-  license      = {MIT}
-}
-```
-
----
-
-## 🚀 Acknowledgements
-
-- **[Ultralytics YOLO11](https://github.com/ultralytics/ultralytics)** — Base architecture and training framework
-- **[Kaggle](https://www.kaggle.com)** — Training infrastructure (Dual T4 GPU)
-- **Aamchi City — Datahack 4** — Hackathon context and dataset
-
----
-
-*Built for the Aamchi City AI Civic System — Datahack 4, PS2 Core ML*