YOLOv5 Model for Face and Hands Detection
Overview
This repository contains a YOLOv5 model trained for detecting faces and hands. The model is based on the YOLOv5 architecture and has been fine-tuned on a custom dataset.
Model Information
- Model Name: yolov5-face-hands
- Framework: PyTorch
- Version: 1.0.0
- Model List ["face", "null1", "null2", "hands"]
- The list model used is 0 and 3 ["0", "1", "2", "3"]
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Usage
Installation
pip install torch torchvision
pip install yolov5
Load Model
import torch
# Load the YOLOv5 model
model = torch.hub.load('ultralytics/yolov5', 'custom', path='path/to/your/model.pt', force_reload=True)
# Set device (GPU or CPU)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
# Set model to evaluation mode
model.eval()
Inference
import cv2
# Load and preprocess an image
image_path = 'path/to/your/image.jpg'
image = cv2.imread(image_path)
results = model(image)
# Display results (customize based on your needs)
results.show()
# Extract bounding box information
bboxes = results.xyxy[0].cpu().numpy()
for bbox in bboxes:
label_index = int(bbox[5])
label_mapping = ["face", "null1", "null2", "hands"]
label = label_mapping[label_index]
confidence = bbox[4]
print(f"Detected {label} with confidence {confidence:.2f}")
License
This model is released under the MIT License. See LICENSE for more details.
Citation
If you find this model useful, please consider citing the YOLOv5 repository:
@misc{jati2023customyolov5,
author = {Damar Jati},
title = {Custom YOLOv5 Model for Face and Hands Detection},
year = {2023},
orcid: {\url{https://orcid.org/0009-0002-0758-2712}}
publisher = {Hugging Face Model Hub},
howpublished = {\url{https://huggingface.co/DamarJati/face-hand-YOLOv5}}
}