End-to-end machine learning workflows using TensorFlow and Labelbox

After performing a TFRecord export, Labelbox provides us with a link to an export.json There are two keys in this file: tfrecord_paths contains Google Cloud Storage URIs to the exported TFRecords (the full list is truncated for brevity) and legends contains a mapping between class labels and the pixel value representing that class within the exported segmentation maps. Leveraging the tf.data.TFRecordDataset API and Google Cloud Storage (GCS)filesystem support, we can quickly and succinctly specify a dataset input pipeline while uses the TFRecord exports Labelbox has stored on GCS: The _parse_tfrecord function uses the schema documented here to deserialize tf.train.Examples from our TFRecords and decode the images into [width, height, 3] (three channels because these images have RGB colorspace) and labels into [width, height, 1] Tensors of float32s. The _resize function returns a function that uses bilinear interpolation to resize images and labels to have width and height both equal to image_dim (512 in our pipeline). An iterator over training_dataset can then be used as an input node into a TensorFlow graph, allowing us to train our model directly on the data collected on Labelbox.