from ultralytics import YOLO

# Load the trained model
model = YOLO('Aun3223/e-waste-battery-anomaly-detection-yolov8n') # or specify local path if downloaded

# Run inference on an image
results = model('path/to/your/image.jpg')

# Show results
for r in results:
    print(r.boxes) # Print bounding box detections
    r.show()       # Display the image with detections

Training Artifacts

Architecture: YOLOv8n
Task: Object Detection
Classes: normal_battery, swollen_battery
Training Data: Custom dataset of simulated e-waste images.

This repository also contains additional training artifacts:

best.pt: The best performing model weights.
args.yaml: The full configuration and arguments used for training.
results.csv: Detailed performance metrics per epoch.
confusion_matrix.png: Visualization of the model's classification performance.
results.png: Training plots showing loss, accuracy, etc.