A pretrained if an ad contains a woman classifier that sorts an image into one of 2 categories. Use the if an ad contains a woman API immediately, no training required, then adapt it to your own data when you need more.
Drop in a photo and get the prediction back. No signup, no setup.
A sample of the 2 labels this pretrained classifier chooses between.
Need a label that isn't here? Clone the classifier into your Nyckel console and edit the label set to fit your data.
Once you've added this classifier to your console, you get your own copy of it behind your own endpoint. Invoke it with any HTTP client:
curl
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer $NYCKEL_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "https://example.com/photo.jpg"}'Python
import requests
# Get an access token: https://www.nyckel.com/docs/api/overview/authentication/
token = "YOUR_ACCESS_TOKEN"
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer " + token},
json={"data": "https://example.com/photo.jpg"},
)
print(response.json())Example response
{
"labelName": "Contains Woman",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if an ad contains a woman categories, served on Nyckel's own infrastructure — your image stays on Nyckel.
Send an image URL or file to the invoke endpoint; the response is a label with a confidence score.
Clone it, then correct predictions and add your own samples in the console — Nyckel retrains automatically, turning this into a custom model tuned to your data.
By identifying whether an ad contains a woman, businesses can enhance their targeted advertising strategies. For instance, brands focused on women's products can ensure that their ads feature female representations, thereby increasing relevance and engagement with their audience.
Platforms that host advertisements can utilize this classifier to aid in content moderation. By automatically flagging ads that do or do not meet specific criteria regarding female representation, platforms can ensure compliance with diversity and inclusivity policies.
Marketers can analyze consumer engagement data based on gender representation in ads. By correlating ad performance with the presence of women, brands can gain insights into how gender visibility influences consumer choices and preferences.
Companies can use this identifier to track and report diversity metrics in their advertising campaigns. This can support corporate social responsibility (CSR) initiatives and help brands align their advertising strategies with public expectations regarding representation.
Advertisers can leverage this function for A/B testing different ad creatives, determining the impact of featuring women versus not featuring women. By understanding which versions resonate better, marketers can refine their creative strategies for increased effectiveness.
Many industries face regulations concerning diversity in advertising. This identifier can aid companies in ensuring their ads comply with relevant industry standards and guidelines around representation, mitigating the risk of backlash or legal challenges.
Businesses can assess influencer content by identifying the presence of women in their posts and ads. This allows companies to align their brand partnerships with influencers who share similar values in representation, fostering stronger and more authentic collaborations.
A zero-shot classifier uses a large foundation model's general knowledge to pick between your labels — no task-specific training, so new or edited labels work immediately. A Nyckel-trained classifier has been trained on labeled examples and runs on Nyckel's own infrastructure, which typically makes it faster, cheaper per call, and more accurate on data that resembles its training set. The "Under the hood" section on this page shows which kind this classifier is, and any classifier can be adapted into a trained one by adding your own examples.
Honestly: we can't know in advance — it depends on your data stream and how closely it resembles what this classifier has seen. The reliable way to find out is to measure it on your own data: start invoking the classifier with real traffic, or upload and annotate a set of images in the console — make sure they look like your production data, not idealized examples. Nyckel's evaluation metrics then show you exactly how it performs on that data before you rely on it.
No classifier is perfect, so Nyckel is built around the correction loop: invokes can be captured for review, you confirm or correct predictions in the console, and corrections become training data. Over time the model adapts to your data distribution — accuracy on your traffic improves with use rather than staying fixed.
No. This if an ad contains a woman classifier works out of the box — clone it into your console and you'll have your own API endpoint in under a minute. Training data only enters the picture when you want to adapt it: your corrected predictions and uploaded samples improve the model, and you can also edit the label set to match your needs.
Trying the classifier on this page is free with no signup. Cloning it requires a free account, and the free tier covers your first API calls each month — see nyckel.com/pricing for current limits and paid tiers.
Add this pretrained classifier to your Nyckel console — you'll get a live API endpoint in under a minute, and a path to a custom model when you need one.