💡[Feature]: Bird Species Identification using Deep Learning #Streamlit #1322
Description
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Feature Description
This feature leverages deep learning techniques to identify bird species from images. The user interface is built using Streamlit, providing an interactive and user-friendly experience for uploading images and viewing results.
Use Case
Data Collection: Gather a comprehensive dataset of bird images, labeled with their respective species.
Data Preprocessing: Perform preprocessing steps such as normalization, resizing, and augmentation to prepare the images for the deep learning model.
Model Architecture: Design and implement a convolutional neural network (CNN) model tailored for image classification tasks.
Training and Validation: Train the CNN model on the preprocessed dataset and validate its performance using appropriate metrics such as accuracy, precision, recall, and F1 score.
Streamlit Interface: Develop a Streamlit application to allow users to upload bird images and get real-time predictions on the bird species.
Model Evaluation: Evaluate the model's performance on a separate test dataset to ensure its generalizability.
Deployment: Deploy the trained model and Streamlit application to a cloud platform for easy access.
Benefits
Accuracy: Leverages deep learning to improve the accuracy of bird species identification.
User-Friendly: The Streamlit interface makes it easy for users to upload images and view results.
Scalability: Can be deployed on various cloud platforms and accessed from anywhere.
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Priority
High
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