Welcome to our Hacktoberfest 2023 project! We're excited to have you contribute. This project aims to [briefly describe the goal or purpose of the project].
#hacktoberfest
This project aims to classify images as either a cat or a dog using machine learning techniques. The model has been trained on a dataset of cat and dog images to make predictions on new, unseen data.
Follow these instructions to set up the project on your local machine.
- Python 3.x
- Pip (Python package installer)
-
Clone the repository:
git clone https://github.com/your-username/cat-dog-classification.git
-
Navigate to the project directory:
cd cat-dog-classification
-
Install the required dependencies:
pip install -r requirements.txt
-
Data Preparation:
- Place your cat and dog images in the
data
directory. - Ensure that the images are organized into subdirectories, such as
data/train/cat
,data/train/dog
,data/test/cat
, anddata/test/dog
.
- Place your cat and dog images in the
-
Training:
-
Run the training script to train the model:
python train.py
-
Adjust hyperparameters in the
config.yaml
file as needed.
-
-
Prediction:
-
Use the trained model to make predictions on new images:
python predict.py --image_path path/to/your/image.jpg
-
-
Evaluate:
-
Evaluate the model performance on a test dataset:
python evaluate.py
-
If you would like to contribute to the project, follow these steps:
-
Fork the repository.
-
Create a new branch for your feature or bug fix:
git checkout -b feature/your-feature-name
-
Make your changes and commit them:
git commit -m "Description of your changes"
-
Push your changes to your fork:
git push origin feature/your-feature-name
-
Open a pull request on the original repository.
This project is licensed under the MIT License.