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README.md

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----
-license: mit
----
+---
+license: apache-2.0
+tags:
+- vision
+- object-detection
+- yolo
+- crack-detection
+- infrastructure
+- civil-engineering
+datasets:
+- custom
+metrics:
+- mAP
+- precision
+- recall
+---
+
+# Crack Detection YOLO Model
+
+This repository contains a YOLO-based object detection model specifically trained to identify and localize cracks in various infrastructure surfaces, including concrete walls, floors, and brick facades.
+
+## Model Overview
+
+- **Task**: Object Detection
+- **Class**: `crack`
+- **Architecture**: YOLO (Ultralytics)
+- **Training Epochs**: 100
+- **Input Resolution**: 640x640 (standard YOLO inference)
+
+## Performance & Training Graphics
+
+The following graphics demonstrate the model's training progression and final performance metrics.
+
+### Training Results
+![Training Results](/graph/9afc4826-bbbe-4107-a616-c63699267e78.png)
+*The plots show consistent convergence in both training and validation losses (Box, Cls, DFL) over 100 epochs, with precision and recall stabilizing at high levels.*
+
+### Precision-Confidence Curve
+![Precision-Confidence Curve](/graph/f2f90a21-c218-4d7f-9ccf-39a3403386df.png)
+*The model achieves a precision of 1.00 at a confidence threshold of 0.987, indicating very high reliability in detections at high confidence levels.*
+
+## Inference Examples   
+
+The model has been validated on diverse surfaces showing robust detection capabilities.
+
+### Concrete Surface Detection
+![Concrete Crack Detection](/graph/5617d570-d2ad-485d-97dc-a9f2ed14acc3.jpeg)
+
+### Building & Brick Wall Detection
+![Brick Wall Crack Detection](/graph/99f3ecba-e888-4596-a131-a34f907c4d4a.jpeg)
+
+## Usage
+
+To use this model with the Ultralytics YOLOv8 library:
+
+```python
+from ultralytics import YOLO
+
+# Load the model
+model = YOLO('crack.pt')
+
+# Perform inference on an image
+results = model.predict('path/to/your/image.jpg', save=True, conf=0.5)
+
+# View results
+for result in results:
+    result.show()
+```
+
+## Dataset Information
+
+The model was trained on a specialized dataset focused on structural cracks. It includes variations in lighting, surface textures, and crack sizes to ensure better generalization in real-world infrastructure inspections.

crack.pt

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+size 22522595