A pretrained tree types by leaves classifier that sorts an image into one of 10 categories — what type of tree it is. Use the tree types by leaves 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 26 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": "Ash",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 tree types by leaves 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.
Researchers can utilize a tree types by leaves identifier to gather data on tree species diversity in various environments. This allows for better monitoring of biodiversity and ecological changes over time, which is crucial for conservation efforts.
City planners can deploy the identifier in conjunction with urban greenery assessments. By classifying tree species through their leaves, planners can make informed decisions about urban forestry and green space developments to enhance aesthetics and environmental benefits.
Farmers can use the classification function to identify tree species on their land for better management of orchards or agroforestry systems. Understanding the types of trees can lead to optimized harvesting practices and improved sustainability techniques.
The identifier can be integrated into educational platforms for schools and universities, where students can interactively learn about different tree species and their characteristics. This engagement fosters environmental awareness and appreciation among future generations.
Gardening enthusiasts can benefit from a mobile app feature allowing them to identify tree species by their leaves. This can assist in choosing compatible plants for their gardens or understanding the maintenance needs of various trees.
Ecologists can use the identifier to analyze habitats in forested areas, correlating tree species with wildlife populations. This information is vital for understanding species interactions and improving habitat management strategies.
Organizations studying climate change can employ the identifier to track tree species distribution and health over time. Analyzing the data gathered can lead to insights on how climate change affects different tree types, aiding in developing mitigation strategies.
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 tree types by leaves 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.