Model Card for YOLOv8m - Harmful Smoke Detection in Indian Vehicles
This model detects harmful smoke emissions from various types of Indian vehicles. It is built on the YOLOv8m architecture and trained on a custom dataset.
Model Details
Model Description
The YOLOv8m model is designed to identify harmful smoke emissions from different types of Indian vehicles, including Auto-Rickshaws, Bikes, Buses, Cars, Heavy Commercial Vehicles (HCV), Light Commercial Vehicles (LCV), and Totos. The custom dataset used for training was gathered from the internet, focusing specifically on smoke detection in these vehicle categories.
- Developed by: HarshitJoshi
- Model type: Object Detection (YOLOv8m)
Results
The model's performance is evaluated based on several metrics: Precision (P), Recall (R), Mean Average Precision at IoU=0.5 (mAP50), and Mean Average Precision at IoU range 0.5:0.95 (mAP50-95).
Summary
| Class | Images | Instances | Box(P) | Box(R) | mAP50 | mAP50-95 |
|---|---|---|---|---|---|---|
| All | 952 | 1422 | 0.731 | 0.678 | 0.749 | 0.441 |
| Auto-Rickshaw | 119 | 138 | 0.679 | 0.812 | 0.83 | 0.496 |
| Bike | 195 | 278 | 0.89 | 0.643 | 0.809 | 0.412 |
| Bus | 202 | 307 | 0.881 | 0.435 | 0.667 | 0.422 |
| Car | 177 | 300 | 0.832 | 0.797 | 0.843 | 0.486 |
| HCV | 146 | 195 | 0.89 | 0.497 | 0.72 | 0.377 |
| LCV | 40 | 42 | 0.3 | 0.762 | 0.55 | 0.292 |
| Toto | 137 | 162 | 0.642 | 0.796 | 0.826 | 0.598 |
Model Architecture and Objective
The model is based on the YOLOv8m architecture, known for its speed and accuracy in object detection tasks. The primary objective of this model is to detect harmful smoke emissions from various types of vehicles commonly found in India.
- Base Architecture: YOLOv8m
- Dataset: Custom dataset gathered from the internet
- Training Objective: Detect harmful smoke emissions from Indian vehicles