

Convert to

Tensorflow TFRecord

COCO is a common JSON format used for machine learning because the dataset it was introduced with has become a common benchmark.





Tensorflow TFRecord

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

enterprise only

This conversion is only available on an Enterprise plan.

enterprise only

This conversion is only available on an Enterprise plan.

How To Convert COCO JSON to Tensorflow TFRecord

If your labeling tool exported annotations in the

COCO

format, but you’re trying to use a

MobileNet SSD v2

model that needs

TFRecord

annotations, we’ve got you covered. You can convert data between these formats for free in 3 clicks with Roboflow.

Step 1: Create a free Roboflow public workspace

Roboflow is the universal conversion tool for computer vision annotation formats. The Public plan is the best way for those exploring personal projects, class assignments, and other experiments to try Roboflow. To convert your dataset, start by creating a free workspace on the Public plan.

Create Roboflow Workspace

Step 2: Upload your data into Roboflow

Once your account has been created, click Create New Project.

Upload your data to Roboflow by dragging and dropping your

COCO JSON

images and annotations into the upload space.

Step 3: Generate Dataset Version

Next, click "Generate New Version" to generate a new version of your dataset:

You can then apply any preprocessing or augmentation steps to your dataset:

Step 4: Export Dataset Version

After generating, you will be prompted to Export your dataset. You can choose to receive your dataset as a .zip file or a curl download link. Choose

Tensorflow TFRecord

when asked in what format you want to export your data. You will see a dropdown with various options like this:

Congratulations, you have successfully converted your dataset from

COCO JSON

format to

Tensorflow TFRecord

format!

Summary

Want to dive deeper into converting annotation formats with Roboflow? In the tutorial below, we explore how to convert your data in the Roboflow dashboard. We also discuss rejecting annotations
and train-test-validation splits.
‍

Next Steps

Ready to use your new

TFRecord

dataset? Great!

If you're looking for more info, we have a more detailed blog post about creating Object Detection TFRecord files.

Here are some compatible models:

MobileNet SSD v2

This architecture provides good realtime results on limited compute. It's designed to run in realtime (30 frames per second) even on mobile devices.

Faster R-CNN

One of the most accurate object detection algorithms but requires a lot of power at inference time. A good choice if you can do processing asynchronously on a server.

EfficientDet (D7) Tensorflow 2

A scalable, state of the art object detection model, implemented here within the TensorFlow 2 Object Detection API.

Frequently Asked Questions

Is it free to convert

COCO JSON

to

Tensorflow TFRecord

?
‍

Yes! It is free to convert

COCO JSON

data into the

Tensorflow TFRecord

format on the Roboflow platform.
‍

How long does it take to convert

COCO JSON

data to

Tensorflow TFRecord

?
‍

If you have between a few and a few thousand images, converting data between these formats will be quick. But, the time it takes to convert between data formats increases with the more images you have.