Load and Preprocess Data
Import your data and ensure it is ready for deep learning.
What you learned: To load and preprocess data
- Load data using an image datastore
imageDatastorefunction automatically labels the images based on folder names
- You can augment your dataset by adding images of different scale and rotation
- Image-based apps can significantly speed up common preprocessing tasks like cropping, labeling, and registering images
Learn a variety of options for deep learning models.
What you learned: To import a deep learning model and modify it for transfer learning
- Use a variety of pretrained models as a starting point for transfer learning
- Use Deep Network Designer app to interactively alter the model for a new task
- Import models and architectures from TensorFlow™-Keras, TensorFlow 2, Caffe, and the ONNX™ (Open Neural Network Exchange) model format
Train the Model
Use the data and modified network to train a new image classifier.
What you learned: To modify a model for learning
- Choose from a variety of training options, which change the training results
- Models can take a long time to train depending on hardware and dataset size
- Perform deep learning without needing to learn how to create a model from scratch
Test the Model and Visualize Results
Load the model and use the test data to see the accuracy of the model.
What you learned: To test the model on new data
- Classify the test data (set aside in step 1) and calculate the classification accuracy
- Visualize the test data with corresponding labels to ensure model accuracy on new data
- Use Explainable AI techniques like GradCAM to visualize where in the image the model detected a defect.