Awesome
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<!-- *** Thanks for checking out the Best-README-Template. If you have a suggestion *** that would make this better, please fork the repo and create a pull request *** or simply open an issue with the tag "enhancement". *** Don't forget to give the project a star! *** Thanks again! Now go create something AMAZING! :D --> <!-- PROJECT LOGO --> <br /> <div align="center"> <a href="https://github.com/gerardocipriano/Cerberus-Dog-Breed-Classification-and-Body-Localization-PyTorch"> <img src="res/secondLogo.png" alt="Logo" width="200"> </a> <h3 align="center">Cerberus-Dog-Breed-Classification-and-Body-Localization-PyTorch!</h3> <p align="center"> The goal of the project is to develop a PyTorch-based model capable of recognizing and classifying 3 dog breeds and localizing 3 specific body parts within images. <br /> <br /> <br /> <a href="https://Cerberus-Dog-Breed-Classification-and-Body-Localization-PyTorch.tk/">View Demo</a> · <a href="https://github.com/gerardocipriano/Cerberus-Dog-Breed-Classification-and-Body-Localization-PyTorch/issues">Report Bug</a> · <a href="https://github.com/gerardocipriano/Cerberus-Dog-Breed-Classification-and-Body-Localization-PyTorch/issues">Request Feature</a> </p> </div> </div> <!-- TABLE OF CONTENTS --> <details> <summary>Table of Contents</summary> <ol> <li> <a href="#about-the-project">About The Project</a> </li> <li> <a href="#getting-started">Getting Started</a> <ul> <li><a href="#installation">Installation</a></li> </ul> </li> <li><a href="#usage">Usage</a></li> <li><a href="#roadmap">Roadmap</a></li> <li><a href="#license">License</a></li> <li><a href="#contact">Contact</a></li> </ol> </details> <!-- ABOUT THE PROJECT -->About The Project
<br /> <div align="center"> <a href="https://github.com/gerardocipriano/Cerberus-Dog-Breed-Classification-and-Body-Localization-PyTorch"> <img src="res/cerberus-logo.jpg" alt="Logo" width="200"> </a> <p align="center"> </div> Cerberus is a PyTorch-based project for classifying dog breeds and localizing specific body parts within images. Our model is capable of recognizing and classifying 3 dog breeds (Toy Poodle, Beagle, Siberian Husky) and localizing 3 specific body parts (eyes, nose, tail) within images. This repository contains the code and resources for training and evaluating the model, as well as a user interface for interacting with the algorithm. <p align="right">(<a href="#readme-top">back to top</a>)</p> <!-- GETTING STARTED -->Installation
- Just Clone/Download the repo
Usage
Classification and Localization functionalities are available in their respective user interfaces (GUIs).
Classification
To utilize the classification feature, please follow these steps:
- Install the required dependencies mentioned in the .txt file.
- Run the .\main.py file.
- Within the classification GUI, you have the option to create a new alexnet model from scratch. This model can then be used for training, validation, or prediction purposes. If you already possess a pre-existing model, you can select it within the interface. Following the training phase, you can evaluate the model's performance using a cat dataset and examine the results.
Localization
If you wish to utilize the localization feature, please run the .\Object-detection\main.py file. (Note: A unified GUI will be introduced in a future version of the project.)
- In the localization GUI, you can upload an image of a dog and the model. This allows you to precisely identify and localize various body parts.
- Additionally, you can monitor the output in real-time during this process by selecting the "Show model validation data" option.
Roadmap
Mandatory features:
- Recognizing and classifying 3 dog breeds (Toy Poodle, Beagle, Siberian Husky)
- Localizing 3 specific body parts (eyes, nose, tail)
Optional features:
- GUI
License
Distributed under the MIT License. See LICENSE for more information.
Contact
Gerardo Cipriano - gerardo.cipriano@studio.unibo.it <br />
Massimiliano Battelli - massimilian.battelli@studio.unibo.it <br />
Project Link: https://github.com/gerardocipriano/Cerberus-Dog-Breed-Classification-and-Body-Localization-PyTorch
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