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https://github.com/behradsadeghi/flower-classification-efficientnet

Classifying images of flowers into 17 categories using EfficientNet-B0 and PyTorch.
https://github.com/behradsadeghi/flower-classification-efficientnet

cnn deep-learning efficientnet gradient-accumulation image-classification mixed-precision pytorch transfer-learning

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Classifying images of flowers into 17 categories using EfficientNet-B0 and PyTorch.

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# 🌸 Flower Classification using EfficientNet-B0



This project demonstrates a deep learning pipeline to classify images of flowers into 17 different categories using a pre-trained EfficientNet-B0 model. It utilizes PyTorch and TIMM (PyTorch Image Models) for training, validation, and testing, implementing mixed precision training to optimize performance. The dataset used is the [**17 Flower Classes**](https://www.kaggle.com/datasets/aima138/17flowerclasses) dataset.



## 📚 Table of Contents



- [🌟 Introduction](#-introduction)

- [✨ Features](#-features)

- [⚙️ Installation](#️-installation)

- [📂 Dataset](#-dataset)

- [🛠️ Training](#-training)

- [📊 Results](#-results)

- [📝 License](#-license)



## 🌟 Introduction



The goal of this project is to classify flowers into 17 different classes using deep learning. We leverage transfer learning with the EfficientNet-B0 model pre-trained on ImageNet and fine-tune it on our flower dataset. The model training and evaluation are conducted using mixed precision training to reduce memory usage and improve training speed.



## ✨ Features



- **Transfer Learning:** Utilizes the EfficientNet-B0 model pre-trained on ImageNet.

- **Mixed Precision Training:** Reduces memory consumption and accelerates training.

- **Dataset Augmentation:** Resizing and normalization of images to improve model generalization.

- **Gradient Accumulation:** Handles larger batch sizes without exceeding GPU memory.

- **Training, Validation, and Test Phases:** Clearly separated phases for training, validation, and testing to monitor performance.



## ⚙️ Installation



### Prerequisites



Ensure you have the following installed:



- Python 3.7+

- PyTorch

- TIMM (PyTorch Image Models)

- Other dependencies: `numpy`, `PIL`, `matplotlib`, `torchvision`, `tqdm`, `gc`



## 📂 Dataset



The dataset contains images of 17 different flower classes. It is divided into training, validation, and test sets. You can download the dataset from Kaggle using the following link: [17 Flower Classes Dataset](https://www.kaggle.com/datasets/aima138/17flowerclasses).



- **Train Directory:** Contains the images used for training the model.

- **Validation:** A portion (20%) of the training dataset is set aside for validation to monitor model performance and avoid overfitting.

- **Test Directory:** Contains the images used for testing the model.



## 🛠️ Training



The model is trained using a PyTorch script that includes functions for:



- **Loading and Transforming Data:** Resize to 299x299, normalization using ImageNet mean and std.

- **Training Loop:** Implements gradient accumulation for better memory management.

- **Validation:** A separate validation set, comprising 20% of the training data, is used to evaluate the model after each epoch to monitor validation accuracy and loss.

- **Saving Best Model:** The model with the best validation accuracy is saved.



## 📊 Results



- The best model weights based on validation accuracy are saved during training.

- After training, the model was evaluated on the test set to determine its classification accuracy.

- **Test Accuracy:** The model achieved an accuracy of **96.47%** on the test set.



## 📝 License



This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.