A pretrained pride flags classifier that sorts an image into one of 10 categories — what type of pride flag it is. Use the pride flags 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 20 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": "Agender",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 pride flags 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.
This function can be integrated into social media platforms to automatically identify and filter out inappropriate or false representations of pride flags. By ensuring authentic symbols are displayed, the platform can foster a more inclusive environment and reduce instances of misrepresentation.
Online retailers can use this function to verify that the pride flags being sold are authentic and meet design standards before they are published on their sites. This can help in building trust with customers and ensuring a consistent quality of products representing the LGBTQ+ community.
Pride event organizers can utilize this function to ensure that all merchandise, signage, and promotional materials correctly represent pride flags. This can enhance the authenticity of events and ensure that the imagery aligns with the values of the LGBTQ+ community.
This technology can serve as a tool in educational settings to teach about LGBTQ+ history and symbolism. By accurately identifying pride flags, educators can create interactive learning modules that promote understanding and acceptance.
Nonprofit organizations working in LGBTQ+ advocacy can leverage this function to monitor the visibility and representation of pride flags in community resources. This can help them assess the impact of their outreach efforts and adapt strategies to better support and represent the community.
Artists and designers developing digital artworks or AI-generated content can implement this function to ensure that any pride flags included in their works are accurate and respectful. This promotes the careful representation of cultural symbols and helps prevent appropriation or misuse of imagery.
In the development of virtual reality spaces, this function could ensure that pride flags are authentically represented, creating an inclusive atmosphere. This can enhance user experience and engagement by accurately reflecting diversity within virtual communities.
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 pride flags 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.