https://github.com/debugger-nan/dog-breed-analyzer
The "Dog-Breed-Identification-Using-Transfer-Learning" builds an AI to classify dog breeds. It uses transfer learning, adapting a pre-trained deep learning model to recognize features and fine-tune for breed identification. The aim is an AI that identifies dog breeds from images by leveraging powerful existing models.
https://github.com/debugger-nan/dog-breed-analyzer
Last synced: 11 months ago
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The "Dog-Breed-Identification-Using-Transfer-Learning" builds an AI to classify dog breeds. It uses transfer learning, adapting a pre-trained deep learning model to recognize features and fine-tune for breed identification. The aim is an AI that identifies dog breeds from images by leveraging powerful existing models.
- Host: GitHub
- URL: https://github.com/debugger-nan/dog-breed-analyzer
- Owner: Debugger-NaN
- Created: 2025-07-14T17:12:21.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-07-14T17:46:08.000Z (12 months ago)
- Last Synced: 2025-07-14T22:19:02.349Z (12 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 2.6 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🐶 Dog Breed Analyzer
Welcome to the **Dog Breed Analyzer** – your intelligent companion for identifying dog breeds from images! Whether you're a pet owner, dog lover, or just curious, this tool helps you recognize breeds with just one click.
---
## 🚀 Features
- 📸 **Image Upload**
Upload any dog image using a simple and intuitive interface.
- 🧠 **Breed Prediction**
Harnesses the power of machine learning to predict the most likely breed from the image.
- 💻 **User-Friendly Interface**
Clean and interactive design built for ease of use.
---
## 🛠️ How It Works
The Dog Breed Analyzer is powered by a **transfer learning model** trained on a vast dataset of dog images. When you upload an image:
1. The model processes the image.
2. Extracts features using a CNN backbone.
3. Returns the most probable dog breed.
Explore the model and training steps in the [`dog-vision.ipynb`](dog-vision.ipynb) notebook.
---
## ⚙️ Installation & Setup
Get the project running locally with these steps:
### 1️⃣ Clone the repository
```bash
git clone https://github.com/Debugger-NaN/Dog-Breed-Analyzer.git
cd Dog-bread-analyser
```
Install dependencies:
(Please replace [dependency-manager] with pip, conda, or similar, and list specific requirements if known, e.g., pip install tensorflow scikit-learn)
# Example:
# pip install -r requirements.txt
If there isn't a requirements.txt file, you might need to install common libraries for deep learning, such as:
tensorflow or pytorch
scikit-learn
Pillow (for image processing)
flask or django (if it's a web application)
---
## ▶️ Usage
Once you have set up the project locally:
Run the application:
(Specify the command to start the application, e.g., for a Python web app:)
# Example:
# python app.py
Open in your browser:
Access the application through your web browser, typically at http://localhost:5000 (or another port if specified).
Upload an image:
Use the provided interface to upload an image of a dog.
View results:
The application will display the predicted dog breed.
> 📝 **Note:** You can see all the steps in the [`dog-vision.ipynb`](dog-vision.ipynb) notebook.
# Contributors
This project was developed by:
Suryansh Sapehia (23BAI11228)
Karan Singh Negi (23BAI11904)
Pragati Singh (23BAI11161)
## Tools:
| Library | Installation Command |
| -------------- | -------------------------------------- |
| Pandas | `conda install pandas` |
| Matplotlib | `conda install matplotlib` |
| NumPy | `conda install numpy` |
| Scikit-Learn | `conda install scikit-learn` |
| TensorFlow | `pip install tensorflow` |
| TensorFlow Hub | `pip install --upgrade tensorflow-hub` |
| Flask | `pip install flask` |