|
|
--- |
|
|
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. |
