---
tags:
- vision
- image-classification
- vit
- facial-expression
- emotion
- sentiment-analysis
- education
- student
- engagement
- understanding
- course-evaluation
- online-learning
- confusion-detection
base_model: trpakov/vit-face-expression
license: unlicense
language:
- en
pipeline_tag: image-classification
---

# 🧠 gowdaman-student-emotion-detection

## Model description

This is a **fine-tuned version of [`trpakov/vit-face-expression`](https://huggingface.co/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
}