Draw boxes around different objects in an image.
Draw polygons around different objects in an image.
Validate imported labels by correcting annotations.
Segment an image into different specified parts.
Validate model output by correcting annotation and assigning classes.
Classify the contents in an image.
Classify text by categories such as sentiment, industry etc.
Judge model-generated output to train your automotive AI.
Rank images from best to worst based on the prompt.
Generate completions based on prompts with domain experts.
Filter images to be labeled based on user-defined criteria
Generate captions for an image.
Create sketches with varying fidelity based on prompts.
Receive annotation feedback as we surface label noise – edge cases and rare events – for you.
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Our clients work with Dataloop, Roboflow etc etc. Share the process here?
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If there's one thing that makes SUPA stand out, it's their commitment to providing consistent support throughout the data labeling process. The team actively and efficiently engaged with us to ensure any ambiguity in the dataset was cleared up.
Everything from uploading data to seeing it labeled in real time was really cool. This is just way simpler to use compared to Amazon Sagemaker and LabelBox. I was also very impressed with how the platform delivered exactly what we needed in terms of label quality.
I was also able to view the labels as they were being generated, which gave me quick feedback about the label quality, rather than waiting for the whole batch. This replaced my standard manual QA process using external tools like Voxel's Fiftyone, as the labels were clear and easy to parse through in real-time.
The annotators were really quick. I would upload and 5 minutes later - 10 images done. I checked 5 minutes later - 100 images done.
The team at [SUPA] has been very professional & easy to work with since we started our collaboration in 2019. They've provided us with good quality judgments to train, tune, and validate our Search & Recommendations models.
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