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Image Classification

Xircuits Image Classification Project Template

Template Setup​

You will need python 3.9+ to install xircuits. We recommend installing in a virtual environment.

Libraries setup​

To install the required libraries Run: 

$ pip install -r requirements.txt

Launch​

To launch Xircuits Run:

$ xircuits

More detailed information on installation, setup and features can be found on Xircuits

Image Classification​

In this template, you will able to classify images of different objects by using transfer learning from a pre-trained network.

We will leverage the pre-trained model in two ways to train our custom classification model:

  1. Feature Extraction: Use the representations learned by a previous pre-trained model to extract meaningful features from new samples. We add a new classifier head, which will be trained from scratch, on top of the pre-trained model so that we could repurpose the feature maps learned previously for the dataset.

  2. Fine-Tuning: we don't need to (re)train the entire model as the base convolutional network already contains features that are generically useful for classifying pictures. However, the final, classification part of the pre-trained model is specific to the original classification task, and subsequently specific to the set of classes on which the model was trained. Unfreeze a few of the top layers of a frozen model base and jointly train both the newly-added classifier head layers and the last layers of the base model. This allows us to "fine-tune" the higher-order feature representations in the base model in order to make them more relevant for the specific task.

This template follows the image classifier training workflow.

  • Examine and understand the data
  • Build an input pipeline
  • Compose the model
  • Load in the pre-trained base model (and pre-trained weights)
  • Stack the classification layers on top
  • Train the model
  • Evaluate model
  • Save model

object_classification_template.xircuits​

  • In this template we download the cats_and_dogs_filtered dataset from Tensorflow and perform a simple binary image classification model training and fine-tuning.

Template

Notice:​

If you would like to use your own dataset, it should follow this structure: 

<Dataset_folder>
    |_train
        |_Class-1
            |_image-1_class-1
            |_image-2_class-1
            |_...
        |_Class-2
            |_image-1_class-2
            |_...
        |_class-3
        |...
    |_validation
        |_Class-1
            |_image-1_class-1
            |_image-2_class-1
            |_...
        |_Class-2
            |_image-1_class-2
            |_...
        |_class-3
        |...