This project is an image classification model designed to identify birds species using deep learning techniques. The model is built using TensorFlow and Keras, and it is deployed on a Streamlit web application, where users can input image URLs for prediction.
- Data: The model is trained using a dataset containing images of various bird species.
- Dataset:
https://www.kaggle.com/datasets/gpiosenka/100-bird-species - Model: The model is a Convolutional Neural Network (CNN) optimized to classify images of Bird species with high accuracy.
- Deep Learning: Utilizes TensorFlow and Keras for building and training the CNN model.
- Streamlit Integration: The model is deployed on a Streamlit app, making it accessible via a web interface.
- URL-based Predictions: Users can paste any image URL to get predictions on whether the image is of a Bird Species.
- Open the Web App: Launch the Streamlit app using the command above or use my app
https://birds-image-classification-model-3gtmbjda9chcmmhphcb3nk.streamlit.app/. - Input Image URL: Copy and paste an image URL into the input box. The image should be a direct link to a bird image.
- Predict: Click "Enter" button to see the model's prediction.
- Architecture: The model is a CNN with multiple layers including convolutional layers, pooling layers, and dense layers.
- Input Dimensions: The images are resized to 180x180 pixels before being fed into the model.
- Training: The model is trained on a well-organized dataset with separate directories for training, validation, and testing.
- URL: Use this example image URL:
https://github.com/KushxKalsi/Birds-Image-Classification-Model/blob/main/Images/Crow.jpg?raw=true - APP URL: https://birds-image-classification-model-3gtmbjda9chcmmhphcb3nk.streamlit.app/
- Prediction: The model will predict "Bird_Species".