👁 b3.png

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.

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

👁 download (1).png

---

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

pip install -q transformers torch pillow gradio

---

Inference Code

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.

Base model

google/siglip2-base-patch16-224

(119)

new exps • 3 items • Updated 1 day ago • 1