| license: mit | |
| datasets: | |
| - food101 | |
| language: | |
| - en | |
| pipeline_tag: image-classification | |
| # Food Vision with EfficientNet | |
| This repository contains the code for the Food Vision project using EfficientNet. The project involves building a deep learning model to classify food images into 101 different classes using the Food101 dataset. | |
| ## Project Overview | |
| This project utilizes TensorFlow and EfficientNet for image classification. It involves training a model on the Food101 dataset, fine-tuning the model, and evaluating its performance. | |
| ## Dataset | |
| The [Food101 dataset](https://www.tensorflow.org/datasets/catalog/food101) is used for this project. It consists of 101,000 images across 101 food classes. | |
| ## Data Preprocessing | |
| The dataset is preprocessed using TensorFlow Datasets (TFDS). Images are resized, normalized, and batched to create an efficient input pipeline for the model. | |
| ## Model Architecture | |
| The EfficientNetV2B0 architecture is used as the base model for feature extraction. The top layers are added for classification. The model is compiled with a suitable loss function, optimizer, and metrics. | |
| ## Training | |
| The model is trained on the preprocessed data, and the training process is logged using TensorBoard. Checkpoints are saved to monitor the model's progress. | |
| ## Fine-tuning | |
| After feature extraction, the model is fine-tuned on the entire Food101 dataset. Learning rate reduction and early stopping callbacks are used to optimize training. | |
| ## Results | |
| The model's performance is evaluated on the test set, and the results are compared before and after fine-tuning. |