# How to create annotations using prodigy for image BB or segmentation / **URL:** \ **Category:** Uncategorized\ **Tags:** image\ **Created:** [May 29, 2018, 9:49am UTC](https://support.prodi.gy/t/how-to-create-annotations-using-prodigy-for-image-bb-or-segmentation/570 "2018-05-29T09:49:03Z")\ **Posts on this page:** 1\ **Showing post:** 2
**Author:** ![ines](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/ines/32/3_2.png) [@ines](https://support.prodi.gy/u/ines)\ **Post date:** [May 29, 2018, 10:29am UTC](https://support.prodi.gy/t/how-to-create-annotations-using-prodigy-for-image-bb-or-segmentation/570/2 "2018-05-29T10:29:56Z")
Hi! Prodigy's manual image interface is still under development. You can see an early demo here: > [@sparkles Demo: fully manual image annotation interface](https://support.prodi.gy/t/demo-fully-manual-image-annotation-interface/215): > > This is a very early and experimental draft – but I like sharing our work in progress to show what we're working on. This interface shows a simple, manual UI for annotating image spans (both rectangular and free-form polygon shapes). I ended up writing the whole thing from scratch, so it's still a bit rough. To illustrate the span annotations the interface is producing behind the scenes, I've added a box underneath the image. This is obviously just for demo purposes and won't be present in Prod… If you have annotated bounding boxes using a different tool and want to import them to Prodigy to re-annotate them or train a model in the loop, you can convert them to Prodigy's JSON format. This thread has some more details and strategies: > [@Using Prodigy to train a new Computer Vision object detection model](https://support.prodi.gy/t/using-prodigy-to-train-a-new-computer-vision-object-detection-model/368/2): > > Yes, this is definitely possible – you’ll just have to plug in your own model implementation. The [LightNet](https://github.com/explosion/lighnet) codebase (our Python port of [DarkNet](https://github.com/pjreddie/darknet)) is still very experimental. We did use it internally to try an active learning workflow, and it looked promising, but it’s not production-ready and still fairly brittle, which is why there’s currently no built-in image.teach recipe, and only the image.test to try out the image interface on your own data. Instead of LightNet, you probably want to use a … In my comment here, I've also shared a converter recipe for the [VGG Image Annotator](http://www.robots.ox.ac.uk/~vgg/software/via/): > [@image classification output for Yolo](https://support.prodi.gy/t/image-classification-output-for-yolo/536/3): > > Yes, it doesn't yet work out of the box, but you can still use Prodigy to train an image model in the loop. We did experiment with the YOLO models a lot (and even wrote our own little Python wrapper for Darknet – but it's not that stable). With Prodigy, we first wanted to focus on the NLP capabilities, since this is what we know best – but computer vision is definitely on our radar and something we're actively working on for both the downloadable tool, as well as the upcoming annotation manager. … --- _[View the full topic](https://support.prodi.gy/t/how-to-create-annotations-using-prodigy-for-image-bb-or-segmentation/570)._