Pixel Spotter: Deep Learning for Precise Pixel Localization

May 1, 2025 AI & Deep Learning
Pixel Spotter Project

Introduction

The Pixel Spotter project presents an interesting challenge in the field of computer vision and deep learning: predicting the exact coordinates of a single white pixel (value 255) in a 50x50 grayscale image where all other pixels are black (value 0). This seemingly simple task provides valuable insights into the capabilities and limitations of different deep learning architectures.

Problem Statement

The core challenge was to develop a deep learning model that could accurately predict the (x,y) coordinates of a single white pixel in a 50x50 grayscale image. The task required:

  • Generating a synthetic dataset of 50x50 images with a single white pixel
  • Designing and training a model to predict pixel coordinates
  • Evaluating different model architectures and hyperparameters
  • Comparing performance between custom and pre-trained models

Technical Approach

I implemented two different approaches to solve this problem:

1. Baseline CNN Model

  • 3 CNN layers with ReLU activation
  • MaxPool layers for downsampling
  • 2 Fully Connected layers with Dropout
  • Final layer predicting (x,y) coordinates

2. Pre-trained ResNet18 Model

  • Fine-tuned pre-trained ResNet18 architecture
  • Added custom input layer for 50x50 images
  • Modified final layer for coordinate prediction
  • Leveraged transfer learning benefits

Implementation Details

The project was implemented using:

  • Python as the primary programming language
  • PyTorch for deep learning implementation
  • NumPy for data generation and manipulation
  • PIL for image processing

Training Process

Key aspects of the training process:

  • Generated 2500 training samples
  • Used Mean Squared Error (MSE) loss function
  • Employed Adam optimizer
  • Experimented with different learning rates (0.0001, 0.001)
  • Varied number of epochs (16, 32, 64)

Results and Analysis

The project yielded interesting results:

  • Baseline CNN achieved good accuracy with 64 epochs and learning rate 0.001
  • Pre-trained ResNet18 outperformed the baseline model significantly
  • Higher learning rates helped converge faster
  • More epochs generally improved performance
Baseline Model Results ResNet18 Model Results

Challenges and Solutions

Key challenges faced during development:

  • Limited Dataset Size: Addressed through careful model architecture selection
  • Overfitting: Mitigated using dropout layers and regularization
  • Precision Requirements: Solved through careful hyperparameter tuning

Future Improvements

Potential areas for future enhancement:

  • Implementing YOLO-based approach for bounding box prediction
  • Exploring SVM for regression task
  • Increasing dataset size and diversity
  • Implementing ensemble methods

Conclusion

The Pixel Spotter project demonstrates the effectiveness of deep learning in solving precise localization tasks. The comparison between custom CNN and pre-trained ResNet18 architectures provides valuable insights into the trade-offs between model complexity and performance. The project highlights the importance of proper model selection, hyperparameter tuning, and the benefits of transfer learning in computer vision tasks.

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