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https://github.com/adesoji1/food-image-classification
Food Image Classification using TensorFlow: A deep learning model to classify various food items using TensorFlow and CNNs.
https://github.com/adesoji1/food-image-classification
colab-notebook hyperparameter-optimization hyperparameter-tuning keras-tensorflow matplotlib-pyplot numpy pandas pillow python3 pytorch regularization tensorflow transfer-learning transformer
Last synced: 17 days ago
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Food Image Classification using TensorFlow: A deep learning model to classify various food items using TensorFlow and CNNs.
- Host: GitHub
- URL: https://github.com/adesoji1/food-image-classification
- Owner: Adesoji1
- License: mit
- Created: 2023-08-21T17:42:29.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-08-21T18:07:24.000Z (about 1 year ago)
- Last Synced: 2023-08-21T19:23:43.927Z (about 1 year ago)
- Topics: colab-notebook, hyperparameter-optimization, hyperparameter-tuning, keras-tensorflow, matplotlib-pyplot, numpy, pandas, pillow, python3, pytorch, regularization, tensorflow, transfer-learning, transformer
- Language: Jupyter Notebook
- Homepage:
- Size: 16.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Food-Image-Classification
### Task instruction below
[here](https://www.figma.com/file/b1X9waDcm6Ygh2PcRvaUI0/Image-Classification?type=design&node-id=0-1&mode=design)
---
### **GitHub Description**:
🍔 Food Image Classification using TensorFlow: A deep learning model to classify various food items using TensorFlow and CNNs.
---
### **README.md**
### Food Image Classification using TensorFlow
This repository contains a deep learning model that classifies various food items using TensorFlow and Convolutional Neural Networks (CNNs).
## Project Overview
The goal of this project is to build a model that can accurately classify images of food into predefined categories. With the rise of health and fitness apps, such a model can be integrated into applications to automatically detect and log consumed food items based on user-uploaded images.
## Dataset
The dataset used for this project consists of images of various food items categorized into different classes. Each image is labeled with its corresponding food category. Available [here](https://drive.google.com/drive/u/0/folders/1fTBPKhOU5bTIo6gTmJmvzDXCT5fXUvTz)
## Features
- **Data Augmentation**: To artificially increase the size of the training dataset and improve model generalization.
- **Convolutional Neural Networks (CNNs)**: Utilized for feature extraction from images.
- **Regularization**: To prevent overfitting and ensure the model generalizes well to new, unseen data.
- **Transfer Learning**: Leveraged pre-trained models to improve accuracy and reduce training time.
## Requirements
- TensorFlow 2.x
- Python 3.7+
- Numpy
- Matplotlib
- Scikit-learn
- hypopt
- PIllow
- torch (During Experimentation)
- pipreqs
## Usage
1. Clone the repository:
```
git clone https://github.com/your_username/food-image-classification.git
```
2. Navigate to the project directory and install the required packages:
```
cd food-image-classification
Use pipreqs to obtain requirements
```
3. Run the main script in the orderr represented in the AI Algorithm .ipynb here on google colab to train the model:
`
4. To evaluate the model on test data, view the test scores in the AI Algorithm.ipyb file:
## Results
The model achieved a test accuracy of 92% on the test dataset. The training and validation loss/accuracy plots can be found in the AI Algotithm file.
## Future Work
- Integrate the model into a mobile application for real-time food classification.
- Expand the dataset to include more diverse food items from various cuisines.
- Experiment with more advanced architectures and techniques to further improve accuracy.
## License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.
## Acknowledgments
- Special thanks to the creators of the food dataset.
- TensorFlow documentation and community for valuable resources and discussions.
---