Question 1

What does the image endpoint return?

Accepted Answer

A JSON body with the predicted label and its confidence, per the chute's playground notes. The underlying Falconsai/nsfw_image_detection model is binary: its config defines exactly two classes, normal and nsfw.

Question 2

What toxicity categories does the text endpoint cover?

Accepted Answer

The endpoint returns an overall label plus a scores object with seven per-category 0-1 scores: toxicity, severe_toxicity, obscene, identity_attack, insult, threat, and sexual_explicit. That label set matches Detoxify's unbiased checkpoint (unitary/unbiased-toxic-roberta), trained on the Jigsaw challenges including Unintended Bias.

Question 3

How do I send an image?

Accepted Answer

Base64-encode the image bytes and POST them as the image_b64 field in a flat JSON body to https://vonkaiser-nsfw-classifier.chutes.ai/image with your Bearer API key. Common image formats work; the ViT model internally resizes to 224x224.

Question 4

Does the text classifier work in languages other than English?

Accepted Answer

Not reliably. The Detoxify checkpoint behind the endpoint (its label set matches unitary/unbiased-toxic-roberta) was trained on English Jigsaw data. Unitary publishes a separate multilingual Detoxify model, but that is not what the live label set indicates, so validate on your target language before trusting non-English scores.

Question 5

Can I use this commercially?

Accepted Answer

Yes. Both upstream models, Falconsai/nsfw_image_detection and Unitary's Detoxify checkpoints (including unitary/unbiased-toxic-roberta), are Apache-2.0 licensed per their Hugging Face repos, which permits commercial use with attribution and license notice.

Question 6

How accurate is it, and should I trust it unsupervised?

Accepted Answer

The image model card reports fine-tuning on 80,000 images but publishes no benchmark table, and toxicity models are known to carry biases from their training data (the Detoxify authors flag this explicitly). Use score thresholds with a human-review band rather than a single hard cutoff for consequential decisions.

Question 7

How fast and expensive is it compared to using an LLM as a moderator?

Accepted Answer

Much cheaper. Both models are compact (~86M and ~125M parameter) encoder classifiers doing a single forward pass, not autoregressive generation, so you can afford to run them on every upload or message rather than sampling traffic.

Nsfw classifier

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Nsfw classifier

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