Transfer Learning for Image Classification

This repository contains implementations of transfer learning techniques for image classification using TensorFlow, Hugging Face Transformers, and Kaggle Datasets. The experiments explore leveraging pre-trained models and large datasets to build efficient and accurate classifiers with limited custom data.

Contents

1. TransferLearning_Tensorflow

   • Implements transfer learning with TensorFlow and Keras.
   • Uses pre-trained models (e.g., ResNet, MobileNet) for feature extraction and fine-tuning.
   • Demonstrates effective utilization of pre-trained models for multi-class classification tasks.

2. TransferLearning_HuggingFace

   • Applies Hugging Face's pre-trained transformers for image classification.
   • Uses the transformers library to fine-tune Vision Transformer (ViT) models.
   • Highlights the flexibility and performance of Hugging Face tools for transfer learning.

3. TransferLearning_Kaggle

   • Demonstrates integration with Kaggle datasets for transfer learning tasks.
   • Prepares and utilizes datasets directly from Kaggle for model training and evaluation.
   • Includes preprocessing and experimentation with TensorFlow/Keras and other frameworks.

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Key Concepts

Transfer Learning

• Utilizing pre-trained models on a new dataset to save time and computational resources.
• Methods used:
  • Feature Extraction: Using frozen layers of pre-trained models as feature extractors.
  • Fine-Tuning: Retraining specific layers of pre-trained models for domain-specific tasks.

Pre-trained Models

• TensorFlow/Keras Models (e.g., ResNet, MobileNet, EfficientNet).
• Hugging Face's Vision Transformer (ViT).

Dataset Sources

• Standard datasets like ImageNet and CIFAR.
• Custom datasets prepared for specific tasks.
• Kaggle datasets accessed and used for experimentation.

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Dependencies

Install the required libraries using:

pip install tensorflow transformers numpy matplotlib kaggle