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README.md

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+---
+
+# Skin Disease Classification using DINOv2 (ISIC2018)
+
+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.
+
+---
+
+## Model Details
+
+- **Developed by:** Karl1hik
+- **Finetuned from model:** [`facebook/dinov2-base`](https://huggingface.co/facebook/dinov2-base)
+- **Dataset used:** [`ISIC2018`](https://huggingface.co/datasets/surajbijjahalli/ISIC2018)
+- **Task:** Image classification (skin lesion diagnosis)
+- **License:** Apache 2.0
+
+---
+
+## 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
+This model should not be used as a standalone diagnostic tool. Clinical decisions should not rely solely on model predictions.
+
+---
+
+## How to Use
+
+```python
+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()