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https://github.com/minhosong88/cnns_food_classification
This project focuses on using Convolutional Neural Networks (CNNs) to classify food images from the Food-11 dataset.
https://github.com/minhosong88/cnns_food_classification
adam-optimizer cnn-classification keras neural-network optimization python relu selu tensorflow
Last synced: 5 days ago
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This project focuses on using Convolutional Neural Networks (CNNs) to classify food images from the Food-11 dataset.
- Host: GitHub
- URL: https://github.com/minhosong88/cnns_food_classification
- Owner: minhosong88
- Created: 2024-08-11T19:26:06.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-08-11T19:36:28.000Z (3 months ago)
- Last Synced: 2024-10-10T08:23:09.016Z (29 days ago)
- Topics: adam-optimizer, cnn-classification, keras, neural-network, optimization, python, relu, selu, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 2.02 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# CNN-Based Food Image Classification
#### Minho Song
## Overview
This project focuses on using Convolutional Neural Networks (CNNs) to classify food images from the Food-11 dataset. The project involves experimenting with different CNN architectures, evaluating their performance using F-1 scores, and applying transfer learning with ResNet50.
## Dataset
- **Source**: [Kaggle - Food-11 Image Dataset](https://www.kaggle.com/datasets/trolukovich/food11-image-dataset)
- **Description**: The dataset contains 16,643 food images categorized into 11 major food categories. For this project, a random extraction of 3,000 images was performed due to computational constraints.
## Project Structure
- `food_classification.ipynb`: Main Jupyter notebook containing the entire workflow, including data preprocessing, model building, and evaluation.
- `requirements.txt`: List of dependencies required to run the notebook.
- `.gitignore`: Excludes unnecessary files from the repository.
## Key Features
- **Evaluation Metric**: F-1 score was chosen as the evaluation metric to balance precision and recall, which is crucial for minimizing both false positives and false negatives in food categorization.
- **CNN Architectures**: Several CNN architectures were explored, including basic CNN models, ResNet-style models, and transfer learning with ResNet50.
- **Cross-Validation**: Nested cross-validation with stratified k-fold was used to ensure robust model evaluation.
- **Visualization**: Comprehensive visualizations of model performance, including training history and ROC curves.
## How to Use
1. **Clone the repository**:
```bash
git clone https://github.com/minhosong88/convolution_neural_network.git
```
2. **Install dependencies:**
```bash
pip install -r requirements.txt
```
3. **Run the notebook**: Open `food_classification.ipynb` in Jupyter Notebook and execute the cells to reproduce the results.
## Results
The project found that simpler CNN architectures outperformed more complex models like ResNet in this specific task, indicating that model complexity does not always translate to better performance.
## References
- Moro, S., Rita, P., and Cortez, P. (2012). Bank Marketing. UCI Machine Learning Repository. [https://doi.org/10.24432/C5K306](https://doi.org/10.24432/C5K306)
- Kaggle - Food-11 Image Dataset. [https://www.kaggle.com/datasets/trolukovich/food11-image-dataset](https://www.kaggle.com/datasets/trolukovich/food11-image-dataset)