Update README.md

README.md

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-license: mit
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+---
+license: mit
+---
+ViT-Base-Patch16-224-in21k Fine-tuned on Skin Disease Image Dataset
+Model Description
+This model is a fine-tuned version of the google/vit-base-patch16-224-in21k Vision Transformer model for image classification tasks. It has been fine-tuned on a custom skin disease image dataset containing 10 classes of skin diseases.
+Intended Use
+The model is designed to classify images of skin lesions into one of 10 categories of skin diseases. It can be used for educational purposes, research, or as a starting point for further fine-tuning.
+Note: This model is not intended for clinical or diagnostic use. Always consult a qualified healthcare professional for medical advice.
+How to Use
+Installation
+Ensure you have the following packages installed:
+bash
+
+
+pip install transformers
+pip install torch
+pip install torchvision
+pip install Pillow
+Loading the Model
+python
+
+
+import torch
+from transformers import ViTForImageClassification, ViTImageProcessor
+from PIL import Image
+
+# Load the fine-tuned model
+model_name = 'your_username/your_model_name'  # Replace with your actual model path on Hugging Face Hub
+model = ViTForImageClassification.from_pretrained(model_name)
+model.eval()
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device)
+
+# Load the image processor
+image_processor = ViTImageProcessor.from_pretrained(model_name)
+Making Predictions
+python
+
+
+def predict(image_path):
+    # Load and preprocess the image
+    image = Image.open(image_path).convert('RGB')
+    inputs = image_processor(images=image, return_tensors="pt")
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    
+    # Perform inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax(-1).item()
+    predicted_class = model.config.id2label[str(predicted_class_idx)]
+    return predicted_class
+
+# Example usage
+image_path = 'path/to/your/image.jpg'  # Replace with the path to your image
+predicted_class = predict(image_path)
+print(f"Predicted class: {predicted_class}")
+Labels
+The model predicts one of the following classes:
+Eczema
+Warts
+Melanoma
+Atopic Dermatitis
+Basal Cell Carcinoma
+Melanocytic Nevi
+Benign Keratosis-like Lesions
+Psoriasis
+Seborrheic Keratoses
+Fungal Infections
+Dataset
+The model was trained on the Skin Diseases Image Dataset available on Kaggle.
+Dataset Details
+Number of Classes: 10
+Total Images: Approximately 40,000
+Classes Included:
+Eczema
+Warts, Molluscum, and other Viral Infections
+Melanoma
+Atopic Dermatitis
+Basal Cell Carcinoma
+Melanocytic Nevi
+Benign Keratosis-like Lesions
+Psoriasis, Lichen Planus, and related diseases
+Seborrheic Keratoses and other Benign Tumors
+Tinea, Ringworm, Candidiasis, and other Fungal Infections
+Data Preprocessing
+Image Size: Resized to 224x224 pixels
+Normalization: Images are normalized using ImageNet statistics:
+Mean: [0.485, 0.456, 0.406]
+Standard Deviation: [0.229, 0.224, 0.225]
+Data Splits
+Training Set: 70%
+Validation Set: 15%
+Test Set: 15%
+The data was split in a stratified manner to maintain the class distribution across all splits.
+Training Procedure
+Base Model: google/vit-base-patch16-224-in21k
+Framework: PyTorch with Hugging Face Transformers
+Optimizer: AdamW
+Learning Rate: 5e-5
+Batch Size: 16
+Number of Epochs: 5
+Loss Function: Cross-Entropy Loss
+Training Steps
+Model Initialization:
+Loaded the pre-trained ViT model.
+Adjusted the classifier head to match the number of classes.
+Data Loading:
+Created custom datasets for training, validation, and testing.
+Used DataLoader with appropriate batch sizes.
+Training Loop:
+For each epoch:
+Training Phase:
+Processed batches of images and labels.
+Computed loss and performed backpropagation.
+Validation Phase:
+Evaluated model performance on the validation set.
+Evaluation Results
+Validation Accuracy: Approximately 70%
+Test Accuracy: Approximately 71%
+Performance Observations
+The model shows reasonable performance given the complexity of the task and the number of classes.
+Further training, hyperparameter tuning, or data augmentation may improve results.
+Limitations
+Non-Clinical Use: The model is not suitable for clinical diagnostics.
+Data Bias: Potential biases due to class imbalance in the dataset.
+Generalization: The model may not perform well on images outside the dataset domain (e.g., different lighting conditions, image quality).
+Ethical Considerations
+Privacy: Ensure that any images used with this model comply with privacy regulations and that patients cannot be identified.
+Responsibility: Use the model responsibly, acknowledging its limitations and the potential consequences of misclassification.
+Citation
+If you use this model, please cite it as:
+bibtex
+
+
+@misc{vit_skin_disease_model,
+  title={ViT Fine-tuned on Skin Disease Image Dataset},
+  author={Your Name},
+  year={2023},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/your_username/your_model_name}},
+}
+References
+Vision Transformer (ViT) Paper
+Hugging Face ViT Documentation