Create README.md

README.md

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+---
+datasets:
+- DamarJati/face-hands-YOLOv5
+language:
+- en
+tags:
+- yolov5
+- anime
+---
+
+# 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
+
+## Usage
+
+### Installation
+```bash
+pip install torch torchvision
+pip install yolov5
+```
+
+### Load Model
+```bash
+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
+```bash
+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:
+
+```bibtex
+@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}}
+}
+```