A pretrained lizard species classifier that sorts an image into one of 2 categories — which lizard species it is. Use the lizard species 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 30 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": "Anolis Carolinensis",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 lizard species 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.
Conservation organizations can use this function to identify different species of lizards in a particular environment more accurately. This helps in the monitoring of species, identifying endangered ones, and developing appropriate conservation strategies.
Wildlife regulatory bodies can use the 'lizard species' identifier in inspecting illegal pet trade. The function can enable them to accurately identify various lizard species, ensuring endangered or protected species are not being sold.
The Multilabel image classification function could assist researchers when conducting biological studies. Helping to identify different lizard species speeds the research process, especially for field researchers dealing with a large amount of photographic data.
Zoos can utilize the function to correctly classify and tag different lizard species in their care. Ensuring an accurate account of species helps in management, educational purposes, and breeding programs.
Wildlife photographers and publications can use this function to classify and organize their wildlife images based on the lizard species. Such an identifier would be beneficial in creating richer and more accurate content.
Veterinary practices with a focus on exotic pets can benefit from the 'lizard species' identifier to verify species during routine care, treatment, or surgical procedures, ensuring each animal receives the best species-specific care.
Non-profit organizations and educational platforms focusing on wildlife and environmental studies can use the function to educate the public. It can aid people to identify lizard species in their local environment, promoting curiosity, and awareness about biodiversity.
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 lizard species 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.