Update README.md

README.md

@@ -1,7 +1,67 @@
 ---
-license: apache-2.0
-pipeline_tag: image-classification
+language: en
 tags:
-- medical
 - 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})")