Anomaly detection

Pinpoint your model errors to prioritize development

Highlight the Subtle Flaws

While visual generative AI has made incredible strides, it still often stumbles in small, telltale ways—whether it's extra fingers, odd textures, warped reflections, or asymmetries that don’t quite add up. These subtle artifacts can quickly break the illusion for users. Find and highlight these issues through the scrutiny of our observant annotators, helping you identify common failure points so you can prioritize improvements where they matter most.

Fast, Scalable, and Thorough

Thanks to our expansive global workforce, we’re able to process vast quantities of data at speed—up to 100,000 responses per hour. Thus, every task can benefit from deep cross-validation, with dozens to hundreds of independent annotations per sample to ensure nothing slips through the cracks. If you're working with rare or edge-case artifacts, our [needle-in-the-haystack workflows](link to the needle in the haystack solutions page) help you isolate and analyze the few problematic examples hiding in large datasets.

Easy Integration and Instant Results

Our API and python package allows you to quickly and easily evaluate large amounts of images whether it being for ad-hoc checks or integrated directly in the training pipeline

All of this is possible with just a few lines of code

from rapidata import RapidataClient, RapidataSettings

client = RapidataClient()

order = client.order.create_locate_order(
    name="Artifact detection example",
    instruction="Look close, find incoherent errors, like senseless or malformed objects, incomprehensible details, or visual glitches? Tap to select.",
    datapoints=["https://assets.rapidata.ai/eac11c3e-ad57-402b-90ed-23378d2ff869.jpg",
                "https://assets.rapidata.ai/55b8b506-9c95-4cd2-9e95-1b6343543d37.jpg",
                "https://assets.rapidata.ai/04e7e3c6-5554-47ca-bdb2-950e48ac3e6c.jpg",
                "https://assets.rapidata.ai/91d9913c-b399-47f8-ad19-767798cc951c.jpg"],
    responses_per_datapoint=35,
    settings=[
        RapidataSettings.alert_on_fast_response(2500)],
    validation_set_id="6768a557026456ec851f51f9"
).run()

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