Labelbox Tool

[Michael]

Regardlessof your labeling method, Labelbox Annotate is a central place where you can manage all your labeling projects, customize your labeling & quality workflows, and monitor your labeling team's performance.

The editor is the labeling interface purposefully designed to be highly configurable. The editor is the primary tool for creating, viewing, and editing annotations. The labeling editor supports the following media types out of the box:

Users can use Foundry to add tools or classifications onto data rows as an auto-labeling feature. This is set up when a project has data to label and an editor with a set ontology. Once completed, users can enable the Model Assisted Labeling tool and configure an LLM to create annotations.

You can set up customized review steps based on your decided quality strategy in your project's Workflow tab. As you work with large, complex projects, having to review all labeled data rows becomes increasingly time-consuming and expensive.

Then choose the tool(s), name and color you need press on the tick and save your ontology.

I would strongly advise against changing an Ontology for ongoing project this could have unexpected effect (doc ref : Ontologies) .

Hey @saulzar - welcome to the Labelbox community. One thing to mention here about your concern is that depending on the data type, ie PDF, text, image, video, etc, the tools you can use during annotate projects changes.

Thanks @saulzar , just checked the project you have mentioned here, and I can use the tool setup with the segmentation mask with the editor I see when you got the error, curious if you can reproduce it or this was transient?

Labelbox automatically adjusts the editor interface based on the asset type selected for the project.

However, there are some global editor settings and enhancements shared across all of the editor interfaces. These global editor settings are designed to provide an optimal labeling experience for your team through increased customization and quick accessibility.

Attachments can be used to provide supplementary content to any asset to help provide additional context for the labeling team. An attachment applies to an individual asset and may comprise an image, video, text, or HTML content. Multiple attachments can be linked to an singular data row.

Labeling instructions (or Annotation guidelines) are essential for any large-scale labeling operation. Whether your labeling team is in-house or outsourced, machine learning teams can leverage labeling instructions to communicate best practices with their labelers.

Each ontology can have one labeling instructions document. You can attach instructions whenever editing an ontology by clicking the Instructions tab, and you can update the instructions at any time. For details, see Add/update instructions.

Access to additional context and information at the data row level can be extremely helpful to labelers as they label data. Users can click to view a side panel that brings up data row information, providing teams with ample context and easy access to information related to the particular data row being served.

Image overlay can be used to provide labelers with additional view options for the image being labeled. For example, if you have additional cameras capturing images of your subject matter in different formats (greyscale, thermal, etc.), you may want to provide these images as contextual layers to the primary image.

Layers are applied to individual images, and a maximum of 10 layers may be added per image. Image layers are a visualization tool designed to help you view the asset to be labeled in different ways. You may change visual settings on the image layers (e.g., transparency, brightness) and still create annotations on the data row.

Labelbox provides various AI-based tools to help you label faster. By eliminating some of your most manual work, these tools allow you to take advantage of the latest AI technologies to move fast and achieve higher throughput.

This tool is embedded into the segmentation mask tool. To use auto-segment, select a segmentation mask tool, toggle on auto-segment by selecting the magic wand icon or using the hotkey R, and draw a box around an object.

Labelbox will automatically draw a segmentation mask on the object inside the box. Then, you can make edits, as usual, using the segmentation mask's pen tools. For more details, please view the documentation here.

This tool is embedded into the bounding box tool and is only available when labeling video assets. When you draw a bounding box around an object, click Track Object to activate bounding box tracking.

I used LabelBox tool to annotate my segmentation dataset. It has the option to export as .json or .csv file. I am unable to convert the exported .json file into standard coco format to use it in MaskRCNN training.

MaskRCNN uses the VGG image Annotator format and not the standard coco format. I also thought that it would use the coco format but when I tried it did not work. After some research it appears that MaskRCNN only uses the coco weights for the initial weights but uses the VGG image Annotator format for the labels.

But for the co-founders at Labelbox, product building was a slow burn. Although original co-founders Manu Sharma, Brian Rieger and Daniel Rasmuson are all extremely technical (thanks to their background in aerospace and software engineering), their energy in the earliest days of Labelbox was spent on deep customer discovery work, rather than being heads-down building an early prototype.

Now a Series D startup with over $188M in funding, Labelbox has emerged as a leader in the burgeoning industry of data labeling. Its software is used to annotate large batches of data; more specifically, it helps people identify and categorize data. To understand its product, think of a picture of a car. A window pops up and asks if you want to label it as a Tesla versus a Ford. Its technology is then able to label all pictures of Teslas accurately through AI.

Artificial intelligence was not the ubiquitous technology that it is today. In the early 2010s, it was largely limited to clunky yet simple models, that focused on neural networks and training computers to read patterns within a set of data. It was through these basic training models that Sharma first familiarized himself with AI.

His co-founder and current Labelbox COO Brian Rieger had a similar path. The two were both students at the same time and worked on class projects like these together before going their separate ways after college: Sharma took off for product jobs at startups in the Bay Area, and Rieger ended up in a data science role for Boeing in Texas.

This realization was enough to spark the seed idea of a new business to pursue. Sharma started to use his days at Planet soaking up all of the information he could around this problem space, taking notes on how internally the team was exploring ways to go about building an ML infrastructure and scaling AI.

Sharma started to poke around the market to see what alternatives were available as well. There was CrowdFlower, whose technology used human intelligence to do simple tasks such as transcribing text or annotating images to train machine learning algorithms.

Sharma knew from his current job at Planet that at least one internal team was tinkering with building data labeling infrastructure, but to validate their belief that a collaborative data labeling tool was a product people were willing to pay for, they would need to find others.

To get the ball rolling on an early customer discovery process, Sharma and the co-founding team decided to start pitching their idea to a niche subset of experts who worked with artificial intelligence every day.

While the co-founders established some criteria of what made someone a good candidate to talk to and eventually pitch, they decided that they would also need a proper framework to measure any feedback they collected.

To make sure they were doing this in an artful way, Sharma divulges not just what questions to ask in early validation conversations (spoiler alert: it's all about the open-ended ones) but why founders (especially technical ones) can benefit from them.

For founders looking to get creative around building awareness and product discovery, Sharma shares a few more of the savvy ways his team was able to bring Labelbox to the customers, not the other way around:

Within a matter of days, Labelbox found its first users. For Sharma, the next logical step was to immediately start testing the waters on price. This commercial-oriented mindset proved to be advantageous in bringing in early revenue.

Sharma and the team were strategic about tracking each of the early users they saw using the product, and they leapt at the chance to ask them for feedback. They emailed these early customers to ask questions like:

By collecting early feedback, they were able to finetune exactly what features in their product they could put behind a paywall, and gauge just how much interest there would be to pay for something like Labelbox.

Many of the early customers that found Labelbox through Wikipedia links and community chat forums were enterprises. Publishing giant Cond Nast was among the first to reach out to Sharma and his team asking to sign a contract.

The founders got into a familiar rhythm. The team would build out new features (mainly at the request of their companies), promote them to their mailing list of existing customers, and follow up with additional outreach to the rest of the inbound requests in the pipeline.

It was a process that was running fairly smoothly, but in order for Labelbox to move at an even greater clip, they needed more engineers. Sharma and his co-founders set out for their first round of fundraising.

The original Labelbox idea has expanded into a multi-product suite. In 2020, Labelbox released a product called Boost, which is a marketplace for data labeling services. And in early 2021, Model was released as a debugging and performance-enhancing tool. Their data and analytics tool, Catalog, was released in 2022. There were always undercurrents of us trying out these ideas, but it really felt like we had the most product-market across our whole suite in the past few years.

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