Tensorflow TFRecord

Tensorflow TFRecords are a binary format used with the TensorFlow Object Detection models.

Overview

The Tensorflow Object Detection API (opens in a new tab) uses a proprietary binary file format called TFRecord. Creating TFRecord files has long been the bane of many developers' existence. While they are very efficient for TensorFlow's deep learning framework to parse, they are quite opaque and are not human readable. They're quite finicky; If something is wrong, it's not easy to find out why.

It's not really possible to create these files by hand. You'll need to use a tool to create them. The most foolproof tool to use is Roboflow because, unlike most one-off shell scripts, Roboflow is a universal converter for computer vision formats.

With Roboflow you can seamlessly convert whatever filetype your annotation tool exports directly into a TFRecord that's ready to use for training your model.

Supported Models

Below, see model architectures that require data in the Tensorflow TFRecord format when training a new model.

  • MobileNet SSD v2 (opens in a new tab)Object Detection

    MobileNet SSD v2 combines MobileNetV2 and SSD for real-time object detection on mobile and edge devices with a compact, efficient architecture.

  • Faster R-CNN (opens in a new tab)Object Detection

    Faster R-CNN introduced the Region Proposal Network for near-real-time two-stage detection, with the canonical implementation available through Meta's Detectron2.

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