The dataset for this project can be found and downloaded at the following Kaggle link: Dog Breed Identification. Ensure you have access to Kaggle and download the dataset before running the project.
This project explores using deep learning techniques to identify dog breeds from images. It utilizes image preprocessing, model training, and evaluation strategies tailored for computer vision tasks.
This project demonstrates an end-to-end pipeline for deep learning-based image classification using Python and TensorFlow/Keras.
- Image Preprocessing: Resizing, normalization, and augmentation of dog breed images.
- Model Architecture: Building and training a convolutional neural network (CNN).
- Evaluation: Assessing model accuracy and predictions on unseen data.
Before running the project, ensure you have the following Python packages installed:
tensorflowkeraspandasnumpymatplotlibseabornscikit-learnjupyter
You can install the required packages using:
pip install tensorflow keras pandas numpy matplotlib seaborn scikit-learn jupyter- Clone the repository:
git clone https://github.com/your-username/Dog-Breed-Prediction.git cd dog-breed-identification - Download the dataset from Kaggle:
- Visit the Dog Breed Identification competition page.
- Download the dataset and place it in the appropriate directory.
- Launch Jupyter Notebook:
jupyter notebook dog_vision.ipynb
- Run the cells sequentially to preprocess the data, train the model, and evaluate its performance.
The project provides insights into the effectiveness of deep learning for image classification. Key outputs include:
- Model architecture and training process.
- Predictions and evaluation metrics on test data.
The dataset is sourced from the Dog Breed Identification competition on Kaggle. It contains labeled images of dogs categorized by breed.
Contributions are welcome! Feel free to fork this repository, submit pull requests, or report issues.