Next-gen labeling for
next-gen AI

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Bounding box

Draw boxes around different objects in an image.

A photo of three large farming equipment vehicles in a field. The vehicles have bounding boxes overlaid on top, indicating that the farming equipment is able to be clearly detected.
Object detection


Draw polygons around different objects in an image.

Photograph of a freeway with a view of many vehicles from the rear. The vehicles are annotated with tight polygon annotations.
Object detection

Pre-label validation

Validate imported labels by correcting annotations.

A photo with a french bulldog is on the left. The bulldog has been annotated with a bounding box annotation. On the right side of the image, the UI shows that the bulldog was tagged as a "German shepherd". The user is able to select the annotation and change the label to the correct dog breed.
Object detection


Segment an image into different specified parts.

A photograph of the interior of a room, with a dining table, couch, tables etc. The photograph is overlaid with annotations which segment the room semantically. Each item and pixel in the image has been labeled with an identifying label.
Semantic Segmentation

Segment Anything (SAM) output validation

Validate model output by correcting annotation and assigning classes.

A photograph of a vehicle with annotations. The annotation overlay is sliced into two down the middle. On the left side, it shows the segmented output generated by Segment Anything Model (SAM) - every part of the vehicle has different annotations. On the right side, it shows that the appropriate parts of the vehicle have been grouped into the correct labels, ie "car", "freespace".
Semantic Segmentation

Image classification

Classify the contents in an image.

Photograph of two dachshunds, or "sausage dogs". Overlaid over the photo is a UI asking the user if the photo is of "Hotdog"?. The user can select "yes" or "no".

Text classification

Classify text by categories such as sentiment, industry etc.

A text bubble shows the text "Wow the food here is bomb" with two emojis at the end. In the bottom right corner, the user is prompted to answer is this is a positive or negative sentiment.

Selecting best model prediction

Judge model-generated output to train your automotive AI.

4 images of the same vehicle are laid out in a grid, labeled "A, B, C, and D". Each vehicle image has a different orthogonal grid overlay. The user is prompted to select which option has the best fit of overlay to the vehicle.

Ranking images

Rank images from best to worst based on the prompt.

There are four images generated by AI laid out in a grid. They have the same prompt - "A man with a bucket hat standing in front of a fountain". The user selects and ranks which images suit the prompt the best by dragging and dropping the options on a list.

LLM generation

Generate completions based on prompts with domain experts.

UI with a Prompt: "Is a number plate person data?" and a section for Completion. The Completion section is shown with placeholder text bubbles.

Data curation

Filter images to be labeled based on user-defined criteria

A photo with various vases in different shapes and sizes. Overlaid on top is UI with the prompt "Should the image be labeled?" "Yes/No". The "Yes" option is selected.
Data curation

Image caption generation

Generate captions for an image.

An painting with an input field beneath it. The input field has the title "Caption", and a description of the painting in the input field.
Generative AI

Sketch generation

Create sketches with varying fidelity based on prompts.

Graphic artwork of a man standing behind a kitchen counter. There are three sketches created from the first graphic, at varying fidelities.
Generative AI