Introduction to TFRecords

Introduction to TFRecords

Introduction to TFRecords – PyImageSearch

“TFRecord is a custom TensorFlow format for storing a sequence of binary records. TFRecords are highly optimized for TensorFlow, which lead to them having the following advantages:

• Efficient form of data storage
• Faster read speed compared to other types of formats

One of the most important use cases of TFRecords is when we train a model using TPU. TPUs are super powerful but require the data they interact with to be stored remotely (usually, we use Google Cloud Storage), and that’s where TFRecords come in. We store the datasets remotely in TFRecord format when training a model on TPU since it makes saving the data efficiently and loading the data easier…”

Source: pyimagesearch.com/2022/08/08/introduction-to-tfrecords/https://pyimagesearch.com/2022/08/08/introduction-to-tfrecords/