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| title: Food-101 Classifier | |
| emoji: 🍔 | |
| colorFrom: yellow | |
| colorTo: red | |
| sdk: gradio | |
| app_file: scripts/app.py | |
|  | |
| [](https://www.python.org/)[](https://pytorch.org/)[](LICENSE) | |
| # 🍽️ Food-101 Image Classification with EfficientNetV2-S and PyTorch Lightning | |
| This repository contains the code for an end-to-end deep learning project to classify 101 food categories from the challenging Food-101 dataset. The project demonstrates a systematic approach to model selection, fine-tuning, and hyperparameter optimization, achieving a final validation accuracy of **85.4%** on the full dataset. | |
| The entire training and evaluation pipeline is built using modern, reproducible practices with PyTorch Lightning. | |
| --- | |
| ## 📑 Table of Contents | |
| - [🍽️ Food-101 Image Classification with EfficientNetV2-S and PyTorch Lightning](#️-food-101-image-classification-with-efficientnetv2-s-and-pytorch-lightning) | |
| - [📑 Table of Contents](#-table-of-contents) | |
| - [🎯 Project Highlights](#-project-highlights) | |
| - [💡 Real-World Applications](#-real-world-applications) | |
| - [🧫 Experimental Results](#-experimental-results) | |
| - [📊 Final Results](#-final-results) | |
| - [🔬 Performance Analysis and Error Diagnosis](#-performance-analysis-and-error-diagnosis) | |
| - [🍤 Lowest-Performing Classes](#-lowest-performing-classes) | |
| - [Root Cause Analysis of Misclassifications](#root-cause-analysis-of-misclassifications) | |
| - [Future Work](#future-work) | |
| - [🧪 Methodology and Experimental Process](#-methodology-and-experimental-process) | |
| - [📁 Repository Structure](#-repository-structure) | |
| - [🚀 Getting Started](#-getting-started) | |
| - [Prerequisites](#prerequisites) | |
| - [Installation](#installation) | |
| - [Usage](#usage) | |
| - [💻 Technologies Used](#-technologies-used) | |
| --- | |
| ## 🎯 Project Highlights | |
| - **High-Performance Model** ⚡: Utilizes a pre-trained `EfficientNetV2-S`, selected for its excellent balance of accuracy and computational efficiency suitable for potential edge deployment. | |
| - **Reproducible Pipeline** 🔄: Encapsulates the entire workflow—from data loading to training and evaluation—in a clean and organized `LightningModule` and `DataModule`. | |
| - **Efficient Experimentation** ⏱️: Overcame hardware limitations by implementing dataset subsetting for rapid prototyping. | |
| - **Advanced Fine-Tuning** 🛠️: Implemented a robust fine-tuning strategy, unfreezing the final three blocks of the feature extractor and using the `Adam` optimizer with a `CosineAnnealingLR` scheduler for stable convergence. | |
| - **In-Depth Analysis** 🔎: Went beyond simple accuracy by calculating and logging per-class F1-scores and accuracies, enabling a deep dive into the model's strengths and weaknesses. | |
| - **Live Deployment** 📺: The final model is deployed and accessible as an interactive Gradio web application on Hugging Face Spaces. | |
| --- | |
| ## 💡 Real-World Applications | |
| Beyond being a technical challenge, this food classification model serves as a foundation for numerous real-world applications in health, hospitality, and smart home technology. | |
| - **Health and Nutrition Tracking** | |
| - **Automated Calorie Counting:** Users can snap a photo of their meal, and an app can automatically identify each food item to provide an instant estimate of calories, macros, and other nutritional information. | |
| - **Dietary Management:** Assists individuals with allergies or specific dietary needs (e.g., diabetes, gluten-free) by helping them identify and log their food intake accurately. | |
| - **Restaurant and Hospitality Tech** | |
| - **Self-Checkout Systems:** In cafeterias or quick-service restaurants, a camera-based system could identify all items on a tray to automate the billing process, reducing queues and improving efficiency. | |
| - **Interactive Menus:** Allow diners to point their phone at a dish to get more information, such as ingredients, allergen warnings, or customer reviews. | |
| - **Smart Home and Appliances** | |
| - **Smart Refrigerators:** A fridge equipped with a camera could identify leftover dishes, suggest recipes based on available food, and help track food spoilage to reduce waste. | |
| --- | |
| ## 🧫 Experimental Results | |
| This project followed an iterative approach. The table below summarizes the key experiments and their outcomes, showing the progression from the initial baseline to the final model. | |
| | Model | Training Strategy | Data % | Key Hyperparameters | Final Val Accuracy | | |
| | :--- | :--- | :--- | :--- | :--- | | |
| | `EfficientNet-B2` | Simple fine-tune (last block) | 50% | `lr=1e-4` | ~64% | | |
| | `EfficientNet-B2` | Unfreeze last 3 blocks | 50% | `lr=1e-3` | 82.0% | | |
| | `EfficientNet-B2` | Two-Stage Fine-Tuning | 50% | `lr1=1e-3`, `lr2=1e-5` | Performance Degraded | | |
| | **`EfficientNetV2-S`** | Unfreeze last 3 blocks | 50% | `lr=1e-4` (Tuned) | 82.4% | | |
| | **`EfficientNetV2-S`** | Unfreeze last 3 blocks and more advanced transforms | 50% | `lr=1e-4` (Tuned) | ~82.4% Pretty much the same Performance| | |
| | **`EfficientNetV2-S`** | **Unfreeze last 3 blocks** | **100%** | **`lr=1e-4` (Tuned)** | **85.4%** | | |
| --- | |
| ## 📊 Final Results | |
| After systematically iterating on model architecture and hyperparameters, the final model achieved the following performance on the full Food-101 validation set: | |
| | Metric | Score | | |
| | :------------------ | :------ | | |
| | Validation Accuracy | **85.4%** | | |
|  | |
| *Here is the Confusion Matrix on the Test set. (you can find this plot in the assets section)* | |
| This model is deployed and accessible as an interactive Gradio web application on Hugging Face Spaces. | |
|  | |
| Check out my [Food101 Gradio Demo](https://huggingface.co/spaces/your-username/food101-demo). | |
| --- | |
| ## 🔬 Performance Analysis and Error Diagnosis | |
| Beyond the aggregate accuracy, a per-class analysis was conducted to identify the model's specific limitations and diagnose the root causes of misclassifications. | |
| The model performed exceptionally well on many classes but struggled with a distinct set of categories, primarily due to visual ambiguity and high variability in appearance. | |
| #### 🍤 Lowest-Performing Classes | |
| The following five classes had the lowest validation accuracy: | |
| | Class Name | Index | Validation Accuracy | | |
| | :------------------ | :---- | :------------------ | | |
| | `shrimp_and_grits` | 93 | 44.0% | | |
| | `ravioli` | 77 | 59.2% | | |
| | `apple_pie` | 0 | 61.6% | | |
| | `huevos_rancheros` | 56 | 63.2% | | |
| | `falafel` | 36 | 63.6% | | |
| #### Root Cause Analysis of Misclassifications | |
| * **High Intra-Class Variation**: The model struggled with dishes that have no single, consistent appearance. | |
| * **Fine-Grained Confusion**: Errors occurred between visually similar classes like `ravioli` vs. `dumplings`. | |
| * **Ambiguous Features**: Foods like `falafel` resemble many small fried dishes, making classification tricky. | |
| #### Future Work | |
| Improvements could include: | |
| - Detailed confusion matrix analysis 🔍 | |
| - More aggressive data augmentation 📈 | |
| - Larger architectures for fine-grained recognition 🏋️ | |
| - Training for longer 🏋️ | |
| --- | |
| ## 🧪 Methodology and Experimental Process | |
| Steps taken in the project: | |
| 1. **Baseline Establishment** 🏁 – EfficientNet-B2 achieved ~64%. | |
| 2. **Architecture Selection** 🏗️ – EfficientNetV2-S chosen for balance of accuracy and size. | |
| 3. **Transforms Selection** 🎨 – TrivialAugmentWide + RandomResizedCrop, RandAugment, etc. | |
| 4. **Fine-Tuning Strategy** 🔧 – Final 3 blocks unfrozen for training. | |
| 5. **Final Model Training** 🏆 – Full dataset, Adam, CosineAnnealingLR, EarlyStopping → 85.4%. | |
| --- | |
| ## 📁 Repository Structure | |
| ```bash | |
| food-101-classification/ | |
| ├── data/ | |
| ├── logs/ | |
| ├── scripts/ | |
| │ ├── main.py | |
| │ ├── models.py | |
| │ ├── class_names.py | |
| │ ├── app.py | |
| │ └── prepare_data.py | |
| ├── .gitignore | |
| ├── requirements.txt | |
| └── README.md | |
| ``` | |
| --- | |
| ## 🚀 Getting Started | |
| ### Prerequisites | |
| - Python 3.10+ 🐍 | |
| - PyTorch 🔥 | |
| - CUDA-enabled GPU (recommended) 🎮 | |
| ### Installation | |
| 1. **Clone the repository:** | |
| ```bash | |
| git clone https://github.com/Deathshot78/Food101-Classification | |
| cd Food101-Classification | |
| ``` | |
| 2. **Install the dependencies:** | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ### Usage | |
| Run training with a subset for quick testing: | |
| ```bash | |
| python main.py | |
| ``` | |
| ### 💻 Technologies Used | |
| - Python | |
| - PyTorch | |
| - PyTorch Lightning | |
| - TorchMetrics | |
| - Gradio | |
| - Matplotlib & Seaborn | |