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---
license: apache-2.0
datasets:
- prithivMLmods/Brain3-Anomaly-Classification
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- brain
- tumor
- biology
- chemistry
- medical
---
# **Brain3-Anomaly-SigLIP2**
> **Brain3-Anomaly-SigLIP2** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for **multi-class medical image classification**. It is trained to distinguish between different types of **brain anomalies** using the **SiglipForImageClassification** architecture.
```py
Classification Report:
precision recall f1-score support
brain_glioma 0.9853 0.9725 0.9789 2000
brain_menin 0.9361 0.9735 0.9544 2000
brain_tumor 0.9743 0.9480 0.9610 2000
accuracy 0.9647 6000
macro avg 0.9652 0.9647 0.9647 6000
weighted avg 0.9652 0.9647 0.9647 6000
```
---
## **Label Space: 3 Classes**
The model classifies each image into one of the following categories:
```
0: brain_glioma
1: brain_menin
2: brain_tumor
```
---
## **Install Dependencies**
```bash
pip install -q transformers torch pillow gradio
```
---
## **Inference Code**
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Brain3-Anomaly-SigLIP2" # Replace with your model path if different
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Label mapping
id2label = {
"0": "brain_glioma",
"1": "brain_menin",
"2": "brain_tumor"
}
def classify_brain_anomaly(image):
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
prediction = {
id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=classify_brain_anomaly,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=3, label="Brain Anomaly Classification"),
title="Brain3-Anomaly-SigLIP2",
description="Upload a brain scan image to classify it as glioma, meningioma, or tumor."
)
if __name__ == "__main__":
iface.launch()
```
---
## **Intended Use**
**Brain3-Anomaly-SigLIP2** can be used for:
* **Medical Diagnostics Support** – Assisting radiologists in identifying brain anomalies from MRI or CT images.
* **Academic Research** – Supporting experiments in brain tumor classification tasks.
* **Medical AI Prototyping** – Useful for healthcare AI pipelines involving limited anomaly classes.
* **Dataset Annotation** – Pre-label brain images for manual review or semi-supervised learning. |