Training Guide

Running the System

1. Test YOLOv8 Inference by downloading the models from Ultralytics

   python detection.py --model yolov8n.onnx --source data/images/horses.jpg
   python detection.py --model yolov8n.onnx --source data/videos/road.mp4
   python detection.py --model yolov8n.onnx --source 0

   *NOTE: Get the detection.py from ultralytics github page and for yolov8

2. Prepare and Get Labelled Dataset from Roboflow

   • Upload images to Roboflow and label them as either fall or nofall.
   • Discard any images that are not relevant by marking them as null.
   • Download the structured dataset from Roboflow and select YOLOv8 for model type when prompted.
   • Extract the zip file from roboflow

3. Train the Model

   • Modify the name for the current operation.
   • Adjust the parameters value to properly utilize the GPU.

   yolo detect train model=yolov8n.pt data="path/to/dataset" imgsz=224 device=0 workers=2 batch=16 epochs=100 patience=50 name=yolov8_fallsafe_detection

4. Continue Training after Pause OR Further Train model with new/updated Dataset

   yolo detect train model=runs/classify/yolov8_fallsafe_detection/weights/last.pt resume=True