Image classification

The task of assigning a label to an entire image, indicating what object or scene is present in the image.
Author

Benedict Thekkel

Dataset

  • CIFAR-10 and CIFAR-100
    • CIFAR-10: 60,000 32x32 color images in 10 classes, with 6,000 images per class.
    • CIFAR-100: Similar to CIFAR-10 but with 100 classes containing 600 images each.
    • Use Case: Benchmarking small-scale image classification models.
  • ImageNet
    • Description: Over 14 million images across 1,000 classes.
    • Use Case: Large-scale image classification, used for pre-training models.
  • MNIST and Fashion-MNIST
    • MNIST: 70,000 grayscale images of handwritten digits (0-9).
    • Fashion-MNIST: 70,000 grayscale images of fashion items in 10 categories.
    • Use Case: Benchmarking simple image classification models.
  • COCO (Common Objects in Context)
    • Description: 330,000 images with objects segmented into 80 categories.
    • Use Case: Object detection, segmentation, and image classification.
  • SVHN (Street View House Numbers)
    • Description: Over 600,000 32x32 color images of house numbers from Google Street View.
    • Use Case: Real-world digit classification.

Models

Convolutional Neural Networks (CNNs)

  • LeNet: One of the earliest CNN architectures, used for digit recognition.
  • AlexNet: Won the 2012 ImageNet competition, significantly deep with ReLU activations.
  • VGGNet: Known for using very small (3x3) convolution filters, 16-19 weight layers.
  • GoogLeNet (Inception): Uses a network-in-network architecture, significantly reducing parameters.
  • ResNet (Residual Networks): Introduces residual connections to train very deep networks.
  • DenseNet: Each layer receives input from all previous layers, promoting feature reuse.

Vision Transformers (ViT)

  • Description: Adapted the Transformer architecture to image classification tasks.
  • Use Case: Competes with CNNs on image classification benchmarks.

EfficientNet

  • Description: Uses a compound scaling method to uniformly scale width, depth, and resolution.
  • Use Case: Balances accuracy and efficiency, outperforming many existing models.

Hyperparameters

  1. Learning Rate
  • Description: Controls the step size at each iteration while moving towards a minimum of the loss function.
  • Tuning: Start with a moderate value (e.g., 0.001), use learning rate schedules (e.g., step decay, exponential decay).
  1. Batch Size
  • Description: Number of samples processed before the model is updated.
  • Tuning: Common values range from 32 to 256. Larger batches require more memory but can leverage better parallelism.
  1. Number of Epochs
  • Description: Number of complete passes through the training dataset.
  • Tuning: Monitor validation loss to avoid overfitting, typically between 10 to 100 epochs.
  1. Optimizer
  • Popular Choices: SGD, Adam, RMSprop.
  • Tuning: Adam is a good default choice; try SGD with momentum for potentially better convergence.
  1. Regularization Parameters
  • Weight Decay (L2 Regularization): Penalizes large weights, reducing overfitting.
  • Dropout Rate: Randomly sets a fraction of input units to 0 at each update during training, preventing overfitting.

Loss Functions

  1. Cross-Entropy Loss
  • Description: Measures the performance of a classification model whose output is a probability value between 0 and 1.
  • Use Case: Standard for multi-class classification problems.
  1. Mean Squared Error (MSE)
  • Description: Measures the average of the squares of the errors between predicted and actual values.
  • Use Case: More common in regression, but sometimes used in classification problems with continuous labels.
  1. Categorical Hinge Loss
  • Description: Measures the performance for “one-versus-all” classification tasks.
  • Use Case: Useful in scenarios with class imbalance.
  1. Focal Loss
  • Description: Modifies cross-entropy loss to address class imbalance by down-weighting the loss assigned to well-classified examples.
  • Use Case: Effective in highly imbalanced datasets.

Other Important Topics

  • Data Augmentation
    • Description: Techniques to artificially increase the size of a dataset by creating modified versions of images.
    • Methods: Rotation, flipping, scaling, cropping, color jittering.
    • Use Case: Helps improve model generalization.
  • Transfer Learning
    • Description: Using a pre-trained model on a new, but related task.
    • Approach: Fine-tuning the pre-trained model on the new dataset.
    • Use Case: Effective when the new dataset is small.
  • Evaluation Metrics
    • Accuracy: Proportion of correctly predicted instances.
    • Precision, Recall, F1-Score: Useful for class imbalance scenarios.
    • Confusion Matrix: Provides insight into the performance of the classification model on each class.
  • Model Interpretability
    • Grad-CAM (Gradient-weighted Class Activation Mapping): Visualizes which parts of the image are important for the model’s predictions.
    • SHAP (SHapley Additive exPlanations): Explains the output of machine learning models.
Back to top