Transfer Learning and Image Classification
This Branch provides an overview of popular image classification datasets and highlights the use of transfer learning for building efficient and accurate models.
Workflow:
Load a pre-trained model (e.g., VGG16 or ResNet) without the top classification layer.
Add custom layers tailored to the specific dataset (e.g., fully connected layers for 102 flower categories).
Freeze the pre-trained layers (optional) to retain their learned features.
Fine-tune the model with the dataset-specific images.
- Cats vs Dogs Dataset : The Cats vs Dogs dataset is widely used for binary classification tasks in machine learning.
Key Features:
Images: ~25,000 images of cats and dogs. Image Size: Original images vary in size; typically resized (e.g., 224x224 pixels) for model training.
Labels: 0: Cat 1: Dog
This dataset is ideal for binary classification experiments and demonstrates the effectiveness of models leveraging transfer learning.
- Oxford Flowers 102 Dataset : The Oxford Flowers 102 dataset is commonly used for flower classification tasks.
Key Features:
Number of Classes: 102 flower categories.
Number of Images: ~8,189 high-resolution images.
Distribution: Each category contains 40–258 images, making it suitable for classification tasks with diverse data.
This dataset tests the ability of transfer learning models to handle varied backgrounds, lighting, and orientations.
- CIFAR-10 Dataset : The CIFAR-10 dataset is a benchmark dataset for image classification.
Key Features:
Number of Classes: 10 categories (e.g., airplanes, cars, birds, etc.).
Number of Images: 60,000 32x32 color images (50,000 training and 10,000 testing).
Preprocessing: Normalized pixel values and categorical labels.
Transfer Learning : Transfer learning leverages pre-trained models to improve training efficiency and accuracy, especially when labeled data is limited or the dataset is small.
Key Advantages:
Pre-trained Knowledge: Models like VGG16, ResNet, and EfficientNet, trained on large datasets (e.g., ImageNet), bring learned features that can generalize to new tasks.
Reduced Training Time: By reusing feature extraction layers, training is faster and computationally efficient.
Improved Accuracy: Pre-trained models help achieve better results, even with smaller datasets.
Example Use Case : In this repository, transfer learning was implemented with the CIFAR-10 dataset using the pre-trained VGG16 an AlexNet models. The base model was modified to suit the dataset's input size and number of classes.
By leveraging transfer learning, even datasets with limited samples, like Oxford Flowers 102 or Cats vs Dogs, can achieve robust performance.