We've also been working on a similar tool (although does not output to YOLO format): https://www.labelbox.io/
We've open sourced the labeling interface for anyone to easily build and support labeling any data - as long as it can be loaded in a browser. Learn more here: https://github.com/Labelbox/Labelbox
About Labelbox:
Labelbox is a enterprise grade and cloud based tool to easily label data for machine learning. Labelbox streamlines data labeling workflow, from micro labeling projects for quick R&D to production grade projects requiring hundreds of collaborators. It is agnostic to data type and has open source labeling frontend with already built templates for image classification & segmentation and text classification. One can label any other kind of datasets by creating a custom labeling interface with javascript API (labeling-api.js). Additional feature includes exporting data in JSON/CSV with auto generated image masks, project & team management and labeling analytics.
The idea is to use OpenCV so that later it uses SIFT and Tracking algorithms to make labeling easier. I wanted this tool to give us automatic suggestions for the labels!
You can go through a video frame-by-frame with YouTube, and it's worth doing it for the video on their landing page for moments such as: https://i.imgur.com/5tRzFsZ.jpg
Key to annotation is using pre trained or partially trained object detectors along with similarity search to quickly highlight unlabelled, similar objects in a dataset. Think of group labelling instead of labeling one object at a time. One of the open source implementations of object search out there is https://github.com/beniz/deepdetect/tree/master/demo/objsear... and that you can build your own group annotation tool around.
On a side note: I am working on a client side annotation tool which is backed by OpenCV and Mask R-CNN. It is still in the early stages (and may contain some bugs), but in case you are interested, you can check it out here [1]
This is really cool! Have you tried using superpixel segmentation? It works well for some biological applications. Grabcut is also immensely useful.
We've been working on a platform for medical image and video annotation tasks. This means we need to support everything from DICOM to large pathology images, and endoscopy videos. We also have it connected to deep learning networks (e.g. Inception v3, YOLO, ENet) so you can easily train or download the JSON for offline analysis.
If you are using Mac OS X, you can use RectLabel.
An image annotation tool to label images for bounding box object detection and segmentation.
https://rectlabel.com
Key features:
Drawing bounding box, polygon, and cubic bezier
1-click buttons make your labeling work faster
Customize the label dialog to combine with attributes
Settings for objects, attributes, hotkeys, and labeling fast
What makes this more appealing than a web interface? Presumably you still need a service to manage the data, and deploying to labelers will be difficult.
Thanks for the comment so the idea is to use OpenCV so that later it also supports video format and uses SIFT and Tracking OpenCV algorithms to make labeling easier.
We've also been working on a similar tool (although does not output to YOLO format): https://www.labelbox.io/
We've open sourced the labeling interface for anyone to easily build and support labeling any data - as long as it can be loaded in a browser. Learn more here: https://github.com/Labelbox/Labelbox
About Labelbox:
Labelbox is a enterprise grade and cloud based tool to easily label data for machine learning. Labelbox streamlines data labeling workflow, from micro labeling projects for quick R&D to production grade projects requiring hundreds of collaborators. It is agnostic to data type and has open source labeling frontend with already built templates for image classification & segmentation and text classification. One can label any other kind of datasets by creating a custom labeling interface with javascript API (labeling-api.js). Additional feature includes exporting data in JSON/CSV with auto generated image masks, project & team management and labeling analytics.