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model-demo

MobileNet Fine-Tuning on Food-101

This project involves fine-tuning a MobileNet model on the Food-101 dataset to optimise performance while managing limited computational resources. Given these constraints, experiments and hyperparameter optimisation were conducted on a 10% subset of the training data. Two distinct fine-tuning strategies were explored: single-stage fine-tuning, where the entire model was trained simultaneously, and two-stage fine-tuning, which involved an initial phase of training only the final Fully-Connected layer followed by a phase of training the entire network. The two-stage approach aimed to leverage rapid adaptation of the classifier before fine-tuning the pre-trained layers with more nuanced adjustments. Our results demonstrated that, of the experimental models, single-stage fine-tuning achieved the highest accuracy at 67.3%, while two-stage fine-tuning achieved 66.1%. Ultimately, we applied single-stage fine-tuning (without freezing) for the final model which we trained on the entire dataset, resulting in a validation accuracy of 83.5% and a test accuracy of 82.9%.

This repository includes a Notebook that summarises our methodologies and results, providing insights into the fine-tuning process. It also includes a Flask web application that deploys the trained final model, which predicts one of the 101 food classes in the Food-101 dataset. This allows users to upload their own images and receive predictions on its food class.

Architecture

Project Architecture This project uses a pretrained MobileNetV3Large model with ImageNet weights, which we fine-tune on the Food-101 dataset by replacing the final fully-connected layer to output predictions for the 101 food classes. The model is trained using cross-entropy loss and its performance is evaluated using accuracy.

Experimental Model Performance

Validation performance

  • Single-Stage Fine-tuning: 67.3% val acc
  • Single-Stage Fine-tuning with freezing (i.e. training only the classifier): 53.0% val acc
  • Two-Stage Fine-tuning v1 (with overfit classifier): 63.9% val acc
  • Two-Stage Fine-tuning v2 (without overfitting issue): 66.1% val acc

Final Model Outputs

Prediction examples

Project Structure

mobile-net-food-101/
├── data/
│   └── food-101/
├── src/
│   ├── data_utils.py
│   ├── mobilenet.py
│   ├── train.py
│   └── vis_utils.py
├── templates/
│   └── index.html
├── app.py
├── main.py
└── ...

Data

This model was trained and tested on the FOOD-101 dataset, which consists of 101 food categories with 101,000 images. More information about this dataset can be found on this website. The dataset can be downloaded from here and should be saved in the data folder in the root-level directory.

Scripts

  • src/data_utils.py: Contains functions to handle data loading, transformations, and preparation of the Food-101 dataset for training, validation, and testing.
  • src/mobilenet.py: Implements a custom MobileNet v3 model for classification tasks, allowing the use of pre-trained weights, loading from checkpoints, and freezing layers during training.
  • src/train.py: Contains functions for training the MobileNet model, evaluating its accuracy, and saving the model's checkpoints.
  • src/vis_utils.py: Provides utility functions for organising directories, saving training histories, and generating plots.
  • main.py: Serves as the entry point for the project, managing command-line arguments, initialising the model and data loaders, and orchestrating the training and testing processes.
  • app.py: A Flask application for predicting food class labels from input images using the fine-tuned MobileNet model.

Running main.py and app.py

To train the model on a 10% subset of the dataset (e.g. for hyperparameter optimisation or experimentation), run:

python main.py --mode train --dataset subset_10 --num_epochs 50 --learning_rate 1e-4 --model_name my_model

To train the model on the full dataset, run:

python main.py --mode train --dataset full --num_epochs 50 --learning_rate 1e-4 --model_name my_model

To evaluate your trained model on the test dataset, run:

python main.py --mode test --dataset full --checkpoint_path checkpoint/my_model/best_model.pth.tar --model_name my_model

To deploy your trained model to a web application, run:

python app.py

Steps to Reproduce

  1. Clone this repository:
git clone https://github.com/gordon801/mobile-net-food-101.git
  1. Install the required dependencies:
pip install -r requirements.txt
  1. Train and evaluate your model by following the process in the mobile-net-food-101.ipynb notebook or by running:
python main.py --mode train --dataset full --num_epochs 50 --learning_rate 1e-4 --model_name my_model
python main.py --mode test --dataset full --checkpoint_path checkpoint/my_model/best_model.pth.tar --model_name my_model
  1. Deploy your trained model to a web application and make predictions by uploading new images:
python app.py

References

  • Searching for MobileNetV3. A. Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan, Q.V. Le, H. Adam. In arXiv, 2019. Paper
  • A Data Subset Selection Framework for Efficient Hyper-Parameter Tuning and Automatic Machine Learning. S. Visalpara, K. Killamsetty, R. Iyer. In SubSetML Workshop 2021, International Conference on Machine Learning, 2021. Paper

Acknowledgements