ASCII Letter Classifier Pipeline

A toy transfer learning pipeline that creates an ASCII letter classifier.

Installation

pip install ascii-letter-classifier[cpu]

...or swap in [gpu] at the end to use the tensorflow-gpu package.

Inference quick start

Set pretrained_classifier to true in config to get a pretrained classifier. Then pass in a batch of TensorFlow or numpy images with shape (batch_size, 32, 32, 1) to predict(). If the images are 8-bit integers, divide by 255 to map to floats in [0, 1].

import numpy as np
from ascii_letter_classifier import AsciiLetterConfig, ascii_letter_classifier

x = np.random.randint(
    0, 256,
    size=(1, 32, 32, 1),
    dtype=np.uint8
) / 255

model = ascii_letter_classifier(AsciiLetterConfig(pretrained_classifier=True))

y = model.predict(x).argmax()

Running the pipeline

There are two commands in the pipeline:

1. save-datasets
2. train-model

Each command can be run with with letters CLI, e.g. letters save-datasets. Optionally, they can also both take the path to a YAML config file with overrides. The values in these files override the defaults set in letters/config.py. So for example, to run the pipeline for 5 epochs, you can call letters train-model config/pipeline.yml.