This is a fine-tuned version of trpakov/vit-face-expression specifically adapted for analyzing student engagement and understanding during online or in-person educational sessions.

📌 Model Overview

Model Type: Vision Transformer (ViT)
Base Model: trpakov/vit-face-expression
Task: Binary emotion classification for educational sentiment analysis
Input: Student face image (RGB)
Output Classes:
- Understand: The student appears to understand the concept.
- Not Understand: The student appears to be confused or not following the lesson.

🎯 Use Case

This model is designed for automatic sentiment detection in educational environments, helping instructors evaluate real-time or post-session student engagement by analyzing facial expressions.

It is ideal for:

Online course engagement monitoring
Intelligent Learning Management Systems (LMS)
Post-lecture video analysis for feedback and insights

🧪 Training Details

Fine-tuned on a custom dataset of student facial expressions captured during course sessions
Labels were manually annotated as Understand or Not Understand based on visual indicators of comprehension

📥 Input Format

Image: A single student facial image (preferably frontal and well-lit)
Size: Automatically resized to match the input size expected by the ViT model

📤 Output

json

{
  "label": "Understand",
  "confidence": 0.87
}

gowdaman/gowdaman-emotion-detection

🧠 gowdaman-student-emotion-detection

Model description

📌 Model Overview

🎯 Use Case

🧪 Training Details

📥 Input Format

📤 Output

gowdaman/
gowdaman-emotion-detection