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---
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 # Replace with your actual accuracy
- name: F1 Score
type: f1
value: 0.84 # Replace with your actual F1 score
---
# Vision Transformer for Tumor Classification
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/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
```python
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})") |