Welcome to the Bird Disease Classification Application repository! This application is designed to classify bird diseases using images of bird fecal samples. It utilizes a Keras VGG16 model as the base architecture to achieve accurate disease identification.
Bird diseases pose a significant challenge for avian researchers and conservationists. This application aims to simplify the process of identifying bird diseases by automating the classification using AI and image analysis techniques.
This application follows a data pipeline that downloads bird fecal sample images from GitHub and then utilizes a pre-trained Keras VGG16 model for disease classification. The application offers an end-to-end solution for bird disease identification.
Link to the data set is here Data Set
- Update config.yaml
- Update secrets.yaml [Optional]
- Update params.yaml
- Update the entity
- Update the configuration manager in src config
- Update the components
- Update the pipeline
- Update the main.py
- Update the dvc.yaml
The core of this application is a Keras VGG16 model, which has proven to be effective in image classification tasks. The model's architecture is optimized for capturing intricate features in the images.
The model is trained on the training dataset using a specific set of hyperparameters, including the optimizer and loss function. Data augmentation techniques are applied to enhance the model's generalization capabilities.
The model's performance is evaluated using the validation dataset. The accuracy matrix is calculated to assess the model's ability to classify bird diseases accurately.
There are three buttons named Upload used for uploading the test image, Predict used for predicting the test image, an the last button named Training model is used for processing the whole pipeline starting from the data ingestion till validating the model.
Thank you BAPPY AHMED entbappy for tutorials.