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license: mit |
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datasets: |
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- food101 |
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language: |
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- en |
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pipeline_tag: image-classification |
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--- |
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# Food Vision with EfficientNet |
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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. |
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## Project Overview |
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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. |
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## Dataset |
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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. |
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## Data Preprocessing |
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The dataset is preprocessed using TensorFlow Datasets (TFDS). Images are resized, normalized, and batched to create an efficient input pipeline for the model. |
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## Model Architecture |
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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. |
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## Training |
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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. |
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## Fine-tuning |
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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. |
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## Results |
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The model's performance is evaluated on the test set, and the results are compared before and after fine-tuning. |
