1) Project Title

Transfer Learning with TensorFlow/Keras — CIFAR-10 Classification Using Pretrained CNNs

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2) Problem Statement and Goal of Project

This project demonstrates transfer learning in TensorFlow/Keras by adapting a pretrained convolutional neural network (CNN) for CIFAR-10 image classification. The goals are to:

• Leverage ImageNet-trained models (e.g., VGG16, ResNet50) as feature extractors.
• Implement both frozen-base feature extraction and fine-tuning strategies.
• Showcase best practices for efficiently adapting large models to smaller datasets.

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3) Solution Approach

1. Dataset Loading & Preprocessing

   • CIFAR-10 loaded from tf.keras.datasets.
   • Pixel values normalized to [0, 1].
   • Labels one-hot encoded with tf.keras.utils.to_categorical.

2. Base Model Selection

   • Pretrained model from tf.keras.applications with imagenet weights.
   • include_top=False to remove the original classifier.
   • Input resized to match the base model requirements.

3. Custom Classifier Head

   • GlobalAveragePooling2D → Dense(256, relu) → Dense(10, softmax).
   • Designed to adapt ImageNet features to CIFAR-10 classes.

4. Training Phases

   • Feature Extraction: Base layers frozen, only new classifier layers trained.
   • Fine-Tuning (optional): Top layers of base model unfrozen, trained with a lower LR.

5. Evaluation & Visualization

   • Model summary printed to verify architecture and trainable parameters.
   • Optional plots for training history.

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4) Technologies & Libraries

• Python 3.x
• TensorFlow / Keras
• NumPy
• Matplotlib (for visualizations)

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5) Description about Dataset

• CIFAR-10:

  • 60,000 images (32×32 RGB), 10 classes.
  • Train: 50,000 images | Test: 10,000 images.
  • Classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck.

• Preprocessing: normalization + one-hot encoding.

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6) Installation & Execution Guide

Prerequisites

• Python 3.x
• TensorFlow (GPU optional)

Install

pip install tensorflow numpy matplotlib

Run

1. Open Transfer Learning_me.ipynb in Jupyter/Colab/VS Code.

2. Execute cells sequentially.

3. The notebook will:

   • Load and preprocess CIFAR-10.
   • Load the pretrained model and build a custom head.
   • Train in feature extraction and/or fine-tuning modes.

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7) Key Results / Performance

• Not provided — The notebook focuses on demonstrating the transfer learning process, not maximizing accuracy.

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8) Screenshots / Sample Output

Example: Model Summary Output

Model: "sequential"
_________________________________________________________________
vgg16 (Functional)           (None, 7, 7, 512)         14714688
global_average_pooling2d     (None, 512)               0
dense                        (None, 10)                5130
=================================================================
Total params: 14,719,818
Trainable params: 5,130
Non-trainable params: 14,714,688
_________________________________________________________________

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9) Additional Learnings / Reflections

• Freezing pretrained layers preserves valuable learned features.
• Fine-tuning selectively can further improve domain adaptation.
• Transfer learning enables strong performance on small datasets with reduced compute cost.
• This workflow is adaptable to any image classification task.

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👤 Author

Mehran Asgari Email: imehranasgari@gmail.com GitHub: https://github.com/imehranasgari

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📄 License

This project is licensed under the Apache 2.0 License – see the LICENSE file for details.

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💡 Some interactive outputs (e.g., plots, widgets) may not display correctly on GitHub. If so, please view this notebook via nbviewer.org for full rendering.

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