| --- |
| license: mit |
| language: |
| - en |
| pipeline_tag: image-classification |
| tags: |
| - computer-vision |
| - accident-detection |
| - yolov11 |
| --- |
| # Accident Classifier |
|
|
| ## Model Description |
| This model classifies road-scene images into two categories: **accident** and **no_accident**. It is part of an academic AI-Based Road Accident Detection and Emergency Report Generation System. |
| |
| ## Intended Use |
| - Road-scene image classification for academic demonstrations. |
| - Supporting a prototype accident-analysis workflow. |
| - Educational experimentation with computer vision and deep learning. |
| |
| ## Model Details |
| - **Architecture:** YOLO11n classification model |
| - **Task:** Binary image classification |
| - **Classes:** `accident`, `no_accident` |
| - **Framework:** Python, Ultralytics, PyTorch |
|
|
| ## Evaluation |
| - **Test accuracy:** 98.12% |
| - **Test images:** 1,858 |
|
|
| Results are based on the project's current held-out test dataset. Performance on new, real-world images may differ. |
|
|
| ## Limitations and Safety |
| This model is an academic prototype, not a verified emergency-response system. It may misclassify images and must not be used as the sole basis for emergency decisions. Predictions should be reviewed by a human. It does not automatically contact emergency services. |
|
|
| ## Related Project |
| The model is used in the [AI-Based Road Accident Detection & Emergency Report Generation System](https://github.com/akashakash920692-byte/AI-Accident-Detection-System). |
