(Currently a work in progress)
Learn the fundamentals of AI Computer Vision (CV) by building a simple image classifier using PyTorch. The project will cover key concepts such as loading and processing image data, building a neural network, training a model, and evaluating its performance.
This project focuses on implementing an image classifier from scratch using PyTorch. It’s designed as a hands-on way to explore core concepts in deep learning and computer vision. The classifier will recognize images from a specific dataset (e.g., CIFAR-10, MNIST, or a custom dataset).
The workflow includes:
- Data Preparation: Loading and transforming images.
- Model Architecture: Designing and implementing a neural network.
- Training: Optimizing the model with backpropagation.
- Evaluation: Testing the model on unseen data.
- Visualization: Displaying results, such as accuracy graphs or misclassified images.
- Gain practical experience with PyTorch and its ecosystem.
- Understand how to preprocess image datasets for deep learning.
- Learn to design and train a neural network for image classification.
- Explore techniques for evaluating and improving model performance.
- Familiarize yourself with key computer vision concepts such as convolutional layers and pooling.
- Image Dataset: Use a popular dataset like MNIST, CIFAR-10, or a custom dataset.
- Data Augmentation: Apply transformations (e.g., cropping, flipping, normalization) to improve generalization.
- Neural Network: Build a model with layers such as convolutional layers, ReLU activations, and fully connected layers.
- Training Pipeline: Include loss computation, optimization, and checkpoint saving.
- Evaluation Metrics: Measure accuracy, precision, recall, and visualize a confusion matrix.
- Results Visualization: Show graphs for training/validation loss and accuracy.
- Programming Language: Python
- Framework: PyTorch
- Dataset Handling: torchvision
- Visualization: Matplotlib*, Seaborn*
- Notebook Environment: Jupyter Notebook* or Google Colab*
- Install Python and PyTorch.
- Install required libraries:
torchvision,numpy,matplotlib, etc. - Prepare the dataset and ensure it’s available for training.
- Load the dataset using
torchvision.datasets. - Apply transformations (e.g., resizing, normalization) using
torchvision.transforms. - Split the data into training, validation, and testing sets.
- Define the architecture of your neural network using
torch.nn. - Include convolutional layers, activation functions, pooling layers, and fully connected layers.
- Add dropout layers (optional) to reduce overfitting.
- Define a loss function (e.g., cross-entropy loss).
- Select an optimizer (e.g., SGD, Adam) and specify the learning rate.
- Train the model for several epochs and monitor loss and accuracy.
- Test the model on unseen data.
- Visualize a confusion matrix to analyze model predictions.
- Generate a classification report (precision, recall, F1-score).
- Plot training and validation loss over epochs.
- Display correctly classified and misclassified images.
- Clone the repository:
git clone https://github.com/your-username/image-classifier-pytorch.git cd image-classifier-pytorch