metadata
language:
- en
base_model:
- Ultralytics/YOLOv8
datasets:
- jonalvarez01/reto2g2-dataset
Mobile and Hat Detection Model - YOLOv8
This repository contains a YOLOv8 model trained to detect mobile phones and hats in images.
Model Description
The model was trained using YOLOv8n architecture to detect two classes:
- Mobile phones
- Hats
Training Details
- Base model: YOLOv8n
- Training epochs: 100
- Hardware: CUDA-enabled GPU
- Framework: Ultralytics YOLO
Usage
from ultralytics import YOLO
# Load the model
model = YOLO('path_to_model.pt')
# Perform detection on an image
results = model('path_to_image.jpg')
Training Code
The model was trained using the following script:
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt").to('cuda')
# Train the model
results = model.train(data="data.yaml", epochs=100)
Dataset
The model was trained on a custom dataset containing images of mobile phones and caps. The dataset was structured following YOLO format requirements.
Model Performance Metrics
The model's performance was evaluated over 100 epochs of training. Here are the key metrics:
Confusion Matrix
Precision-Recall Curve
F1-Score Curve
