https://github.com/benitomartin/birds-classification

Computer Vision (Pytorch) Birds Classification
https://github.com/benitomartin/birds-classification

efficientnet gradio jupyter-notebook python pytorch

Last synced: about 2 months ago
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Computer Vision (Pytorch) Birds Classification

• Host: GitHub
• URL: https://github.com/benitomartin/birds-classification
• Owner: benitomartin
• Created: 2023-10-07T16:21:15.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-04-20T18:04:29.000Z (10 months ago)
• Last Synced: 2024-11-08T10:10:02.898Z (3 months ago)
• Topics: efficientnet, gradio, jupyter-notebook, python, pytorch
• Language: Jupyter Notebook
• Homepage: https://huggingface.co/spaces/bmartinc80/birds_pytorch
• Size: 5.67 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# BIRDS CLASSIFICATION 🦅



    



This repository hosts a notebook featuring an in-depth analysis of 2 **Pytorch EfficientNet** models, along with an app deployment using Gradio. The following models were meticulously evaluated:

- EffNetB0

- EffNetB2

The dataset used has been downloaded from [Kaggle](https://www.kaggle.com/datasets/gpiosenka/100-bird-species) and contains a set of 525 bird species. 84635 training images, 2625 test images(5 images per species) and 2625 validation images(5 images per species).

The app can be tested in **Hugging Face** (.py files hosted there) following this [link](https://huggingface.co/spaces/bmartinc80/birds_pytorch). Feel free to ⭐ and clone this repo 😉

## 👨‍💻 **Tech Stack**

![Visual Studio Code](https://img.shields.io/badge/Visual%20Studio%20Code-0078d7.svg?style=for-the-badge&logo=visual-studio-code&logoColor=white)

![Jupyter Notebook](https://img.shields.io/badge/jupyter-%23FA0F00.svg?style=for-the-badge&logo=jupyter&logoColor=white)

![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)

![Pandas](https://img.shields.io/badge/pandas-%23150458.svg?style=for-the-badge&logo=pandas&logoColor=white)

![NumPy](https://img.shields.io/badge/numpy-%23013243.svg?style=for-the-badge&logo=numpy&logoColor=white)

![PyTorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?style=for-the-badge&logo=PyTorch&logoColor=white)

![Matplotlib](https://img.shields.io/badge/Matplotlib-%23d9ead3.svg?style=for-the-badge&logo=Matplotlib&logoColor=black)

![Linux](https://img.shields.io/badge/Linux-FCC624?style=for-the-badge&logo=linux&logoColor=black)

![Git](https://img.shields.io/badge/git-%23F05033.svg?style=for-the-badge&logo=git&logoColor=white)

## 🪶 Set Up

In the first stage, a set of helper functions was created in order to easily perform the modeling and prediction

- **Set seed**: Set random seeds for PyTorch operations, both on the CPU and the GPU

- **DataLoaders**: Create data loaders for training and testing datasets using PyTorch's DataLoader class

- **Writer**: SummaryWriter object for logging experiments and metrics in TensorBoard

- **Training and Testing**: Several functions for training and testing a PyTorch model 

- **Plots**: Several plots including loss curve, predictions, and images

## 📳 Modelling

The first approach was to train 2 Pytorch EfficientNet models (EffNetB0, EffNetB2) with **5 and 10 epochs** using the pre-trained model weights of EffNetB0 for the DataLoaders in order to establish a baseline. The **EffNetB2 with 10 epochs** showed the best performance above **93%** on the test set.



    





    



## ↗️ Model Improvement

Then the EffNetB2 with 10 epochs was trained again but this time using the pretrained model weights of EffNetB2 for the DataLoaders. This time an accuracy above **95%** on the **test set** and above **93%** on the **validation set** was achieved.



    



## 👏 App Deployment

The last step was to deploy an app hosted in Hugging Face using Gradio. This app can be tested with available sample images or with your own ones.