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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-224 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- facial |
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- emotion |
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- detection |
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--- |
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# **Facial-Emotion-Detection-SigLIP2** |
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> **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. |
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```py |
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Classification Report: |
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precision recall f1-score support |
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Ahegao 0.9916 0.9801 0.9858 1205 |
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Angry 0.8633 0.7502 0.8028 1313 |
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Happy 0.9494 0.9684 0.9588 3740 |
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Neutral 0.7635 0.8781 0.8168 4027 |
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Sad 0.8595 0.7794 0.8175 3934 |
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Surprise 0.9025 0.8104 0.8540 1234 |
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accuracy 0.8665 15453 |
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macro avg 0.8883 0.8611 0.8726 15453 |
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weighted avg 0.8703 0.8665 0.8663 15453 |
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``` |
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The model categorizes images into 6 facial emotion classes: |
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Class 0: "Ahegao" |
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Class 1: "Angry" |
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Class 2: "Happy" |
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Class 3: "Neutral" |
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Class 4: "Sad" |
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Class 5: "Surprise" |
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```python |
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!pip install -q transformers torch pillow gradio |
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``` |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor |
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from transformers import SiglipForImageClassification |
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from transformers.image_utils import load_image |
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from PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "prithivMLmods/Facial-Emotion-Detection-SigLIP2" |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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def emotion_classification(image): |
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"""Predicts facial emotion classification for an image.""" |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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labels = { |
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"0": "Ahegao", "1": "Angry", "2": "Happy", "3": "Neutral", |
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"4": "Sad", "5": "Surprise" |
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} |
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predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
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return predictions |
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# Create Gradio interface |
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iface = gr.Interface( |
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fn=emotion_classification, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Prediction Scores"), |
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title="Facial Emotion Detection", |
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description="Upload an image to classify the facial emotion." |
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) |
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# Launch the app |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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# **Intended Use:** |
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The **Facial-Emotion-Detection-SigLIP2** model is designed to classify different facial emotions based on images. Potential use cases include: |
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- **Mental Health Monitoring:** Detecting emotional states for well-being analysis. |
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- **Human-Computer Interaction:** Enhancing user experience by recognizing emotions. |
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- **Security & Surveillance:** Identifying suspicious or aggressive behaviors. |
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- **AI-Powered Assistants:** Supporting AI-based emotion recognition for various applications. |
