Facial-Emotion-Detection-SigLIP2
Facial-Emotion-Detection-SigLIP2 is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to classify different facial emotions using the SiglipForImageClassification architecture.
Classification Report:
precision recall f1-score support
Ahegao 0.9916 0.9801 0.9858 1205
Angry 0.8633 0.7502 0.8028 1313
Happy 0.9494 0.9684 0.9588 3740
Neutral 0.7635 0.8781 0.8168 4027
Sad 0.8595 0.7794 0.8175 3934
Surprise 0.9025 0.8104 0.8540 1234
accuracy 0.8665 15453
macro avg 0.8883 0.8611 0.8726 15453
weighted avg 0.8703 0.8665 0.8663 15453
The model categorizes images into 6 facial emotion classes:
Class 0: "Ahegao"
Class 1: "Angry"
Class 2: "Happy"
Class 3: "Neutral"
Class 4: "Sad"
Class 5: "Surprise"
!pip install -q transformers torch pillow gradio
import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Facial-Emotion-Detection-SigLIP2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def emotion_classification(image):
"""Predicts facial emotion classification for an 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()
labels = {
"0": "Ahegao", "1": "Angry", "2": "Happy", "3": "Neutral",
"4": "Sad", "5": "Surprise"
}
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=emotion_classification,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Prediction Scores"),
title="Facial Emotion Detection",
description="Upload an image to classify the facial emotion."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
Intended Use:
The Facial-Emotion-Detection-SigLIP2 model is designed to classify different facial emotions based on images. Potential use cases include:
- Mental Health Monitoring: Detecting emotional states for well-being analysis.
- Human-Computer Interaction: Enhancing user experience by recognizing emotions.
- Security & Surveillance: Identifying suspicious or aggressive behaviors.
- AI-Powered Assistants: Supporting AI-based emotion recognition for various applications.