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---
license: apache-2.0
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- text-generation-inference
---
# **Face-Confidence-SigLIP2(Experimental)**
> **Face-Confidence-SigLIP2** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for **binary image classification**. It is trained to distinguish between images of **confident faces** and **unconfident faces** using the **SiglipForImageClassification** architecture.
> [!note]
*SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features* https://arxiv.org/pdf/2502.14786
```py
Classification report:
precision recall f1-score support
confident 0.8468 0.8179 0.8321 4872
unconfident 0.8691 0.8909 0.8799 6611
accuracy 0.8600 11483
macro avg 0.8580 0.8544 0.8560 11483
weighted avg 0.8596 0.8600 0.8596 11483
```
## **Label Space: 2 Classes**
The model classifies each image into one of the following categories:
```
Class 0: "confident"
Class 1: "unconfident"
```
---
## **Install Dependencies**
```bash
pip install -q transformers torch pillow gradio
```
---
> Image Scale (Optimal): 256 × 256
## **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/Face-Confidence-SigLIP2" # Replace with your model path if different
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Label mapping
id2label = {
"0": "confident",
"1": "unconfident"
}
def classify_face_confidence(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_face_confidence,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=2, label="Face Confidence Classification"),
title="Face-Confidence-SigLIP2",
description="Upload an image to detect if a face looks confident or unconfident."
)
if __name__ == "__main__":
iface.launch()
```
---
## **Demo Inference(Image)**
## **Intended Use**
**Face-Confidence-SigLIP2** can be used for:
* **Behavioral Analysis** – Detect confidence levels in facial expressions.
* **Education & Training** – Assess learner engagement or self-confidence.
* **HR & Recruitment** – Analyze non-verbal cues during interviews.
* **Dataset Curation** – Separate confident vs unconfident facial images for training. |