Jun 2024 – Jun 2024AI & Data
Image Classifier using Tensorflow
A comprehensive deep learning model building experience producing high validation accuracy by classifying images into organized folders.
Rationale
What I Learned:
- The critical importance of balanced and well-preprocessed datasets.
- Advanced techniques in CNN architecture design and hyperparameter tuning.
- Practical implementation of machine learning models for real-world applications.
I loved learning new things natively manipulating models using Google Colab, Keras, and TensorFlow. Tracking epochs and tuning hyperparameters in loops was a challenging yet highly rewarding experience!
Tech Stack
TensorFlowKerasPythonGoogle ColabCNN
Key Highlights
- ▹Enhanced the dataset with robust data augmentation techniques to drastically improve model accuracy and robustness.
- ▹Built and trained a convolutional neural network (CNN) with optimized layers and dropout regularization.
- ▹Developed an automated pipeline to actively classify images and dynamically move them into respective category folders based on predictions.
- ▹Achieved high validation accuracy, ensuring reliable model performance on unseen test data.
Architecture Details
1. Data Preprocessing & Augmentation
- Applying varied transformations explicitly to expand the dataset variation preventing overfitting and improving real-world generalization.
2. CNN Model Architecture & Automation
- Built on TensorFlow & Keras, introducing Dropouts selectively to penalize excessive weight correlations yielding excellent validation accuracy.
- Enforced an automated OS-level script sorting output files directly matching prediction classes into local directories.