Transfer Learning (Images)
When creating an impulse to solve an image classification problem, you will most likely want to use 'transfer learning' as learning block. This is particularly true especially when working with a relatively small dataset.
Transfer learning is the process of taking features learned from one problem and leveraging it on a new but related problem. Most of the time these features are learned from large scale datasets with common objects hence making it faster & more accurate to tune and adapt to new tasks.
To choose transfer learning as your learning block, go to create impulse and click on 'Add a Learning Block', and select 'Transfer Learning'
Impulse set up for image classification
To choose your preferred pretrained network, go to Transfer learning on the left side of your screen and click 'choose a different model'. A pop up will appear on your screen with a list of models to choose from as shown in the image below.
Choose different model transfer learning model
Edge Impulse uses state of the art MobileNetV1 & V2 architectures trained on ImageNet dataset as it's pretrained network for you to fine-tune for your specific application. The pretrained networks comes with varying input blocks ranging from 96x96 to 320x320 and both RGB & Grayscale images for you to choose from depending on your application & target deployment hardware.
Transfer Learning available models

Neural Network Settings

Before you start training your model, you need to set the following neural network configurations:
NN Settings
  • Number of training cycles: Each time the training algorithm makes one complete pass through all of the training data with back-propagation and updates the model's parameters as it goes, it is known as an epoch or training cycle.
  • Learning rate: The learning rate controls how much the models internal parameters are updated during each step of the training process. Or you can also see it as how fast the neural network will learn. If the network overfits quickly, you can reduce the learning rate
  • Validation set size: The percentage of your training set held apart for validation, a good default is 20%.
You might also need to enable auto balance to prevent model bias or even enable data augmentation to increase the size of your dataset and have more diverse dataset to prevent overfitting.
The preset configurations just don't work for your model? No worries, Expert Mode is for you! The Expert mode gives you full control of your model so that you can configure it however you want. To enable the expert mode, just click on the "⋮" button and toggle the expert mode.
Expert mode
You can use the expert mode to change your loss function, optimizer, print your model architecture and even set an early stopping callback to prevent overfitting your model.
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