---
title: RealtimeMonocularDepthModel
emoji: 👁
colorFrom: yellow
colorTo: pink
sdk: gradio
pinned: false
license: mit
short_description: Real-Time Monocular Depth Estimation for AR
sdk_version: 5.7.1
---

# Real-time Depth Estimation using Knowledge Distillation

This project demonstrates real-time depth estimation using a compressed student model trained through knowledge distillation. Here's how it works:

## Knowledge Distillation
The CompressedStudentModel was trained using knowledge distillation from a larger, more complex teacher model (DPT). This technique allows the smaller student model to learn from the teacher's predictions, effectively transferring knowledge and achieving comparable performance with reduced computational requirements.

## Model Architecture
The student model uses an encoder-decoder architecture optimized for efficient depth estimation:
- Encoder: Extracts hierarchical features through convolutional layers and max pooling.
- Decoder: Upsamples features to produce a high-resolution depth map.

## Real-time Processing
The model is designed for real-time inference on webcam input:
1. Each frame is preprocessed and resized to 200x200 pixels.
2. The frame is passed through the model to generate a depth map.
3. The depth map is visualized as a 3D surface plot using matplotlib.

## 3D Visualization
The depth map is rendered as an interactive 3D surface, providing an intuitive representation of the scene's depth structure. The plot uses a viridis colormap to represent depth values, with warmer colors indicating closer objects and cooler colors for more distant ones.

## Usage
To use this depth estimation tool:
1. Ensure your webcam is connected and functioning.
2. The interface will display your webcam feed and the corresponding 3D depth visualization in real-time.
3. Move objects or your camera to see how the depth map changes dynamically.

This project showcases the potential of compressed models and knowledge distillation in creating efficient, real-time computer vision applications, and could be utilized in Augmented Reality.