{"id":29618609,"url":"https://github.com/debugger-nan/dog-breed-analyzer","last_synced_at":"2025-07-21T02:02:35.837Z","repository":{"id":304703743,"uuid":"1019662398","full_name":"Debugger-NaN/Dog-Breed-Analyzer","owner":"Debugger-NaN","description":"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. 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Whether you're a pet owner, dog lover, or just curious, this tool helps you recognize breeds with just one click.\n\n---\n\n## 🚀 Features\n\n- 📸 **Image Upload**  \n  Upload any dog image using a simple and intuitive interface.\n\n- 🧠 **Breed Prediction**  \n  Harnesses the power of machine learning to predict the most likely breed from the image.\n\n- 💻 **User-Friendly Interface**  \n  Clean and interactive design built for ease of use.\n\n---\n\n## 🛠️ How It Works\n\nThe Dog Breed Analyzer is powered by a **transfer learning model** trained on a vast dataset of dog images. When you upload an image:\n1. The model processes the image.\n2. Extracts features using a CNN backbone.\n3. Returns the most probable dog breed.\n\nExplore the model and training steps in the [`dog-vision.ipynb`](dog-vision.ipynb) notebook.\n\n---\n\n## ⚙️ Installation \u0026 Setup\n\nGet the project running locally with these steps:\n\n### 1️⃣ Clone the repository\n\n```bash\n\ngit clone https://github.com/Debugger-NaN/Dog-Breed-Analyzer.git\ncd Dog-bread-analyser\n\n```\n\nInstall dependencies:\n(Please replace [dependency-manager] with pip, conda, or similar, and list specific requirements if known, e.g., pip install tensorflow scikit-learn)\n\n\n# Example:\n# pip install -r requirements.txt\n\nIf there isn't a requirements.txt file, you might need to install common libraries for deep learning, such as:\n\ntensorflow or pytorch\n\nscikit-learn\n\nPillow (for image processing)\n\nflask or django (if it's a web application)\n\n---\n\n## ▶️ Usage\nOnce you have set up the project locally:\n\nRun the application:\n(Specify the command to start the application, e.g., for a Python web app:)\n\n# Example:\n# python app.py\n\nOpen in your browser:\nAccess the application through your web browser, typically at http://localhost:5000 (or another port if specified).\n\nUpload an image:\nUse the provided interface to upload an image of a dog.\n\nView results:\nThe application will display the predicted dog breed.\n\n\n\n\u003e 📝 **Note:** You can see all the steps in the [`dog-vision.ipynb`](dog-vision.ipynb) notebook.\n\n\n\n# Contributors\nThis project was developed by:\n\nSuryansh Sapehia (23BAI11228)\n\nKaran Singh Negi (23BAI11904)\n\nPragati Singh (23BAI11161)\n\n\n\n## Tools:\n\n| Library        | Installation Command                   |\n| -------------- | -------------------------------------- |\n| Pandas         | `conda install pandas`                 |\n| Matplotlib     | `conda install matplotlib`             |\n| NumPy          | `conda install numpy`                  |\n| Scikit-Learn   | `conda install scikit-learn`           |\n| TensorFlow     | `pip install tensorflow`               |\n| TensorFlow Hub | `pip install --upgrade tensorflow-hub` |\n| Flask          | `pip install flask`                    |\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdebugger-nan%2Fdog-breed-analyzer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdebugger-nan%2Fdog-breed-analyzer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdebugger-nan%2Fdog-breed-analyzer/lists"}