| --- |
| license: mit |
| tags: |
| - computer-vision |
| - object-detection |
| - yolov8 |
| - security |
| - real-time |
| --- |
| |
| # Sentinel AI Crime Model (YOLOv8 Medium) |
|
|
| This is a custom-trained **YOLOv8 Medium** model explicitly designed to detect real-time threats from surveillance cameras. |
|
|
| ### Model Description |
| The Sentinel AI model was trained on thousands of physical crime scene videos and acts as the vision engine for the **Sentinel AI Pipeline**. It is optimized to track background pedestrians while simultaneously isolating high-threat events like physical violence. |
|
|
| - **Developer:** Ayush Yele |
| - **Framework:** PyTorch & Ultralytics YOLOv8 |
| - **Architecture:** YOLOv8 (Medium) |
| - **Epochs Trained:** 100 |
|
|
| ### Classes |
| This model predicts 4 specific macro-classes for emergency dispatch scenarios: |
| - `0`: `fight` (Physical altercations, assault) |
| - `1`: `weapon` (Knives, handguns, blunt objects) |
| - `2`: `violence` (Robbery, vandalism, rioting) |
| - `3`: `normal` (Pedestrians, standing objects) |
|
|
| ### How to Use |
| You can plug this model directly into standard Ultralytics YOLO inference code: |
|
|
| ```python |
| from ultralytics import YOLO |
| |
| # Load the custom trained model |
| model = YOLO("AyushYele/Sentinel_Ai") |
| |
| # Run inference on an image |
| results = model("surveillance_feed.jpg") |
| results[0].show() |
| |
