Dabo App - Dabo is an Android application that leverages a custom-built TensorFlow Lite (TFLite) machine learning (ML) model to perform a specific task.

Project details:

Project Overview: Dabo is an Android application that leverages a custom-built TensorFlow Lite (TFLite) machine learning (ML) model to perform a specific task. The model was developed using Python following a supervised learning paradigm, and the Android app was built using Java.

Technical Details:

1. Data Collection and Preprocessing:
   • Data Sources: Identify and gather relevant datasets for training the ML model.
   • Data Cleaning: Address issues like missing values, outliers, and inconsistencies to ensure data quality.
   • Feature Engineering: Create or extract relevant features from the raw data to improve model performance.
   • Data Normalization: Scale numerical features to a common range to prevent bias.
2. Model Development and Training:
   • Model Architecture: Choose a suitable ML algorithm (e.g., neural network, decision tree, random forest) based on the problem and dataset characteristics.
   • Training Data: Split the dataset into training and validation sets for model evaluation.
   • Hyperparameter Tuning: Experiment with different hyperparameters (e.g., learning rate, batch size) to optimize model performance.
   • Model Training: Train the model on the training data using the chosen algorithm.
3. Model Evaluation and Optimization:
   • Metrics: Use appropriate metrics (e.g., accuracy, precision, recall, F1-score) to evaluate model performance on the validation set.
   • Model Refinement: If necessary, iterate on the model architecture, hyperparameters, or data preprocessing steps to improve results.
4. Model Conversion to TFLite:
   • Export:Convert the trained model to the TFLite format, which is optimized for mobile devices.
5. Android App Development:
   • Integration: Incorporate the TFLite model into the Android app using the TensorFlow Lite Java API.
   • User Interface: Design an intuitive user interface that allows users to interact with the app and provide input for the model.
   • Input Handling: Implement mechanisms to capture user input (e.g., images, text) and preprocess it for the model.
   • Output Display: Visualize the model's predictions or results in a clear and understandable manner.

Technology/Tools Used

 

Keras

 

Tensorflow.js

 

Java

 

Android