AnimalsClassificationModel is a Python-based application that allows users to classify animals by uploading a photo. The model leverages deep learning techniques, specifically the ResNet34 architecture, to accurately identify different animal species. This project is built using a variety of libraries including numpy, pandas, streamlit, plotly, and fastai.
- Image Upload: Users can upload an image of an animal.
- Animal Classification: The application classifies the uploaded image into one of the pre-defined animal categories using a ResNet34 model.
- Interactive Interface: Built with Streamlit, providing an easy-to-use interface.
- Data Visualization: Utilizes Plotly for visualizing the classification results.
Make sure you have the following installed:
- Python 3.7 or higher
- pip (Python package installer)
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Clone the repository:
git clone https://github.com/dostonshernazarov/AnimalsClassificationModel.git cd AnimalsClassificationModel -
Install the required packages:
pip install -r requirements.txt
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Run the Streamlit application:
streamlit run app.py
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Open your browser and navigate to the provided local URL (usually
http://localhost:8501). -
Upload an image of an animal and view the classification results.
- app.py: The main application file for Streamlit.
- model.py: Contains the code for loading and using the ResNet34 model.
- requirements.txt: Lists all the dependencies required for the project.
- data/: Directory to store datasets and pre-trained models.
- utils/: Utility functions for preprocessing and other tasks.
All the dependencies required for this project are listed in the requirements.txt file. Below are the main libraries used:
- numpy: For numerical computations.
- pandas: For data manipulation and analysis.
- streamlit: For building the web interface.
- plotly: For creating interactive visualizations.
- fastai: For building and training the deep learning model.
- torch: PyTorch, the underlying deep learning framework.
To install the dependencies, run:
pip install -r requirements.txt