https://github.com/rickydoan/deep-learning-cnn-calltech101-pretrained-transfer-learning
https://github.com/rickydoan/deep-learning-cnn-calltech101-pretrained-transfer-learning
Last synced: 11 months ago
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- Host: GitHub
- URL: https://github.com/rickydoan/deep-learning-cnn-calltech101-pretrained-transfer-learning
- Owner: RickyDoan
- Created: 2025-01-06T10:49:14.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-06T10:54:51.000Z (over 1 year ago)
- Last Synced: 2025-01-06T11:37:55.465Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🖼️ Caltech101 Image Classification with CNNs and Transfer Learning
📋 Overview
* This project involves image classification on the Caltech101 dataset, exploring both custom CNNs and pre-trained models to classify 102 object categories effectively.
# 🛠 Key Methods and Workflow
#Data Preparation
* Images resized to 128x128 pixels.
* Data augmentation (flips, rotations) applied to improve generalization.
# Custom CNN
* Built a CNN with convolutional, pooling, and fully connected layers.
* Trained for 5 epochs, achieving 45% accuracy.
* Transfer Learning
# Fine-tuned ResNet18 and EfficientNet-B0:
* ResNet18: 63% accuracy.
* EfficientNet-B0: 85% accuracy.
# Evaluation
* Compared accuracy and loss across models.
* Visualized predictions on random images with both true and predicted labels.
# 🔑 Key Results
* Pre-trained models significantly outperform training from scratch.
* EfficientNet-B0 showed the best performance, achieving 85% accuracy in just one epoch of fine-tuning.
* 🖇️ Try It Yourself
* Google Colab Notebook
Feel free to explore the code and share your suggestions! 🚀