Deep Learning for Fruit Classification

Dec 4, 2024

Deep Learning Fruit Classification Project

Introduction

  • Presenter: AK
  • Focus: Classifying fruit images using deep learning.
  • Dataset: Fruits360 from Kaggle.
    • High variety of fruit types for better model results.
    • Dataset size: ~750 MB, requiring substantial GPU and processing power.
    • Previous attempt: 1.5 hours of training, achieved 85% accuracy.
    • Recommendation: Hyperparameter tuning for improved accuracy.

Video Overview

  • Importance of watching the entire video for understanding.
  • Encourage engagement: like, share, comment, subscribe.
  • Personal contact: Instagram ID provided for questions and project assistance.

Project Structure

  1. Loading the Dataset

    • Loading a custom dataset from Kaggle.
    • Recommended to watch previous deep learning video for foundational knowledge.
    • Using CNN (Convolutional Neural Network) for the classification project.

  2. Deep Learning Methodology

    • Importing Libraries
      • Two popular libraries: TensorFlow/Keras and PyTorch.
      • TensorFlow noted for larger community support and ease of finding solutions.
    • Dataset Structure
      • Train and test folders with images.
      • Train: 67,000 images across 131 fruit categories.
      • Test: 22,000 images across the same categories.

Data Insights

  • Visualizing images from the training dataset.
  • Checking image dimensions: 100x100 pixels, RGB channels included.

Building the Neural Network

  • CNN structure already set up; mathematics behind it explained in previous video.
  • Dropout Layer
    • Added before the fully connected layer to prevent overfitting.
    • Randomly shuts down neurons to optimize performance.

Model Compilation

  • Loss Function: Categorical cross-entropy used for multi-class classification.
  • Optimizer: Stochastic Gradient Descent (SGD) to analyze learning rates.
  • Metrics: Evaluates accuracy during training.

Data Augmentation

  • Techniques: shear range, zoom range, horizontal and vertical flips.
  • Purpose: Reducing overfitting by modifying training data.

Training the Model

  • Set up data generators for training and testing.
  • Batch size of 32 for iterative processing.
  • Training Duration: 1.5 hours.

Making Predictions

  • Saving the model for future predictions.
  • Using softmax function to determine probabilities of outcomes.
  • Randomly selecting a test image and predicting its category (e.g., cherry for category 26).

Conclusion

  • Encouragement to like, subscribe, and share for more content.
  • Thank you message from the presenter.