---
license: apache-2.0
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- text-generation-inference
---

![2.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/CPSV18EisjG36vLoxsavm.png)

# **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
```

![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/MotjWiRrjx_5sMpR5LGZK.png)

## **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)**

![Screenshot 2025-09-05 at 05-34-43 Face-Confidence-SigLIP2.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/h9kG6dFzontzdX0FuEHMF.png)
![Screenshot 2025-09-05 at 05-28-14 Face-Confidence-SigLIP2.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/hpIf5Hr0lTXjPPdZpmHAJ.png)
![Screenshot 2025-09-05 at 05-27-24 Face-Confidence-SigLIP2.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/gLPbNYLBMTfYGjVdmIl3E.png)
![Screenshot 2025-09-05 at 05-26-12 Face-Confidence-SigLIP2.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/kZ1AUC9VdTlvGz2cy3238.png)

## **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.