A pretrained gender of scientist classifier that sorts an image into one of 2 categories. Use the gender of scientist 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": "Female Scientist",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 gender of scientist 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.
Organizations can leverage the 'gender of scientist' identifier to analyze the gender representation within their research teams. This data can help institutions uncover patterns, identify disparities, and implement strategies to promote gender diversity in STEM fields.
Universities and companies can use the gender identification function to tailor their recruitment marketing campaigns. By understanding the demographics of potential candidates, they can create more inclusive job postings and outreach efforts to attract underrepresented genders in scientific disciplines.
Educational institutions can utilize the identifier to develop customized learning programs tailored to diverse genders in scientific fields. By analyzing participant demographics, programs can be designed to address unique needs and challenges faced by different gender groups in STEM education.
Publishers can harness the gender classification data to personalize and recommend content to their audience. By analyzing reading patterns associated with different genders, they can curate articles, research papers, and educational resources that resonate more effectively with their readers.
Conferences and workshops can implement the gender identifier to ensure diverse speaker line-ups and panel discussions. By assessing submitted abstracts and speaker nominations, event organizers can strive for balanced representation and inclusivity, enhancing the overall event quality.
Funding agencies can analyze the gender of scientists applying for grants to assess equity in grant allocation. This identification can help highlight disparities, leading to more informed decision-making and policy adjustments to support underrepresented genders in securing funding.
Companies can track the gender distribution of scientists in their workforce to create benchmarks for diversity in their organizations. This metric can serve as a foundation for internal audits and help prioritize diversity initiatives, training programs, and employee resource groups focused on inclusion.
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 gender of scientist 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.