metadata

language: en
tags:
  - vision
  - image-classification
  - medical-imaging
  - tumor-classification
license: apache-2.0
base_model: google/vit-base-patch16-224
model-index:
  - name: vit_tumor_classifier
    results:
      - task:
          name: Image Classification
          type: binary-classification
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.85
          - name: F1 Score
            type: f1
            value: 0.84

Vision Transformer for Tumor Classification

This model is a fine-tuned version of google/vit-base-patch16-224 for binary tumor classification in medical images.

Model Details

Model Type: Vision Transformer (ViT)
Base Model: google/vit-base-patch16-224
Task: Binary Image Classification
Training Data: Medical image dataset with tumor/non-tumor annotations
Input: Medical images (224x224 pixels)
Output: Binary classification (tumor/non-tumor)
Model Size: 85.8M parameters
Framework: PyTorch
License: Apache 2.0

Intended Use

This model is designed for tumor classification in medical imaging. It should be used as part of a larger medical diagnostic system and not as a standalone diagnostic tool.

Usage

from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image

# Load model and processor
processor = AutoImageProcessor.from_pretrained("SIATCN/vit_tumor_classifier")
model = AutoModelForImageClassification.from_pretrained("SIATCN/vit_tumor_classifier")

# Load and process image
image = Image.open("path_to_your_image.jpg")
inputs = processor(image, return_tensors="pt")

# Make prediction
outputs = model(**inputs)
predictions = outputs.logits.softmax(dim=-1)
predicted_label = predictions.argmax().item()
confidence = predictions[0][predicted_label].item()

# Get class name
class_names = ["non-tumor", "tumor"]
print(f"Predicted: {class_names[predicted_label]} (confidence: {confidence:.2f})")