https://github.com/gengo-bit/image-classification-of-fish-species-using-the-cifar-100-dataset
Classifies five fish species from the CIFAR-100 dataset using VGG16 with transfer learning. Achieves 68%-76% accuracy with dropout regularization and learning rate scheduling.
https://github.com/gengo-bit/image-classification-of-fish-species-using-the-cifar-100-dataset
cifar100
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Classifies five fish species from the CIFAR-100 dataset using VGG16 with transfer learning. Achieves 68%-76% accuracy with dropout regularization and learning rate scheduling.
- Host: GitHub
- URL: https://github.com/gengo-bit/image-classification-of-fish-species-using-the-cifar-100-dataset
- Owner: Gengo-bit
- License: mit
- Created: 2024-12-19T14:40:11.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-12-19T15:21:30.000Z (4 months ago)
- Last Synced: 2025-04-12T17:19:35.790Z (11 days ago)
- Topics: cifar100
- Language: Python
- Homepage:
- Size: 331 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Image Classification of Fish Species Using the CIFAR-100 Dataset
This repository showcases a deep learning project aimed at classifying fish species from the CIFAR-100 dataset. Leveraging the power of transfer learning with the VGG16 model, this implementation accurately categorizes images into five fine-grained fish species classes: aquarium fish, flatfish, ray, shark, and trout.
## Features
- **Dataset**: CIFAR-100, filtered for fish species classification.
- **Model Architecture**: Transfer learning using VGG16 with custom classification layers.
- **Training Optimization**: Dropout regularization and learning rate scheduling.
- **Evaluation Tools**: Confusion matrix and accuracy visualization.
- **Visualization**: Training history and prediction results.
## Prerequisites
Ensure you have the following installed:
- Python 3.7 or higher
- TensorFlow 2.x
- Keras
- NumPy
- Matplotlib
- OpenCV
Install all dependencies using:
```bash
pip install tensorflow keras numpy matplotlib opencv-python
```
## Dataset
The CIFAR-100 dataset comprises 60,000 32x32 color images across 100 classes. For this project, only the following fish species classes were selected:
- Aquarium Fish
- Flatfish
- Ray
- Shark
- Trout
The dataset was filtered to include only these classes, and the images were resized to 224x224 to match the input requirements of the VGG16 model.
## Project Structure
```bash
├── Source Code/
│ └── train_fish_cifar100.py # Script for training and evaluating the model
├── README.md # Documentation
├── data/ # Directory for dataset (to be created by the user)
├── results/ # Directory for storing output (e.g., visualizations, metrics)
```
## Model Architecture
The project utilizes a modified VGG16 model with the following components:
- **Pre-trained Layers**: The VGG16 model, pre-trained on ImageNet, is used as the feature extractor with its convolutional layers frozen to retain pre-learned weights.
- **Custom Fully Connected Layers**: Added on top of the pre-trained layers to handle the fish species classification task.
- **Dropout Layers**: Used to reduce overfitting during training by randomly disabling neurons.
- **Softmax Activation**: Ensures probabilistic outputs for multi-class classification.
## Training Configuration
- **Loss Function**: Sparse categorical cross-entropy is used to handle integer-encoded class labels.
- **Optimizer**: Adam optimizer with learning rate scheduling to adapt learning rates dynamically.
- **Epochs**: The model is trained for 20 epochs.
- **Input Size**: Images are resized from 32x32 to 224x224 to match the VGG16 input size requirements.
## How to Run
1. Clone the repository:
```bash
git clone https://github.com/Gengo-bit/Image-Classification-of-Fish-Species-Using-the-CIFAR-100-Dataset
cd Image-Classification-of-Fish-Species-Using-the-CIFAR-100-Dataset
```
2. Prepare the CIFAR-100 dataset by downloading it and organizing the files into a data/ directory.
3. Train the model by running the script:
```bash
cd Source Code
python train_fish_cifar100.py
4. The results, including training metrics and visualizations, will be saved in the results/ directory.
## Results
The model demonstrated an accuracy range of **68% to 76%**.
### Key Insights
- **Training and Validation Loss Trends**:
- Training loss consistently decreases.
- Validation loss stagnates or increases after ~12 epochs, indicating overfitting.
- **Confusion Matrix**:
- Highlights the model's classification performance and areas of misclassification.
- **Visualization**:
- Displays predictions with true vs. predicted labels for test images.
## Challenges & Solutions
- **Overfitting**:
- **Challenge**: The model overfits after a few epochs.
- **Solution**: Dropout layers and reduced model complexity were implemented to mitigate this issue.
- **Long Training Times**:
- **Challenge**: The large dataset and extended epochs increased training time.
- **Solution**: Learning rate scheduling was applied to speed up convergence.
- **Fluctuating Accuracy**:
- **Challenge**: Accuracy during training was inconsistent.
- **Solution**: Hyperparameter tuning and reduced epochs improved stability.
## Authors
- Paul Emmanuel Corsino
- David Emmanuel Lacsao
- Klinnsonveins Yee
## License
This project is licensed under the MIT License. See the LICENSE file for details.
## References
- [CIFAR-100 Dataset](https://www.cs.toronto.edu/~kriz/cifar.html)
- [VGG16 Model Documentation](https://keras.io/api/applications/vgg/#vgg16-function)