Computer Vision Project

🚀 Skin Disease Classification using DINOv2 (ISIC2018)

This model is designed to classify skin lesion images into predefined categories from the ISIC2018 dataset. It fine - tunes the facebook/dinov2 - base Vision Transformer backbone to enhance performance in medical image classification tasks.

🚀 Quick Start

This model classifies images of skin lesions into one of the predefined categories from the ISIC2018 dataset. It is fine - tuned on top of the facebook/dinov2 - base Vision Transformer backbone for improved performance in medical image classification tasks.

✨ Features

• Accurate Classification: Classifies dermatoscopic images from the ISIC2018 dataset into various skin disease categories.
• Fine - tuned Model: Built on the facebook/dinov2 - base backbone for better performance in medical image classification.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch

image = Image.open("your_skin_image.jpg")
processor = AutoImageProcessor.from_pretrained("kar1hik/computer-vision-project")
model = AutoModelForImageClassification.from_pretrained("kar1hik/computer-vision-project")

inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
predicted_class = logits.argmax(-1).item()

Advanced Usage

No advanced usage code examples are provided in the original document, so this part is skipped.

📚 Documentation

Model Details

Uses

Direct Use

This model can be used directly for classifying dermatoscopic images from the ISIC2018 dataset into one of the skin disease categories such as melanoma, nevus, basal cell carcinoma, etc.

Intended Users

• Medical researchers
• Dermatology assistants
• ML practitioners working on medical imaging

Out - of - Scope Use

⚠️ Important Note

This model should not be used as a standalone diagnostic tool. Clinical decisions should not rely solely on model predictions.

🔧 Technical Details

No technical details are provided in the original document, so this section is skipped.

📄 License

This model is licensed under the Apache 2.0 license.