A pretrained if a tree is healthy classifier that sorts an image into one of 2 categories. Use the if a tree is healthy 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": "Healthy Tree",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if a tree is healthy 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.
Cities can use the 'if a tree is healthy' identifier to regularly assess the condition of urban trees. This can help municipal departments prioritize maintenance, identify trees that need treatment, and ensure public safety in parks and green spaces.
Farmers can implement this classification function to monitor the health of trees within orchards or vineyards. By identifying unhealthy trees early, they can take timely action to prevent pests or diseases from spreading and improve overall crop yields.
Environmental organizations can utilize the tree health identifier to assess the vitality of trees in forests and conservation areas. This information helps in developing strategies for reforestation and conservation efforts by highlighting areas that need intervention.
Insurance companies can employ the classification function to evaluate the health of trees on properties. This can facilitate the underwriting process for homeowners' insurance policies and improve claims processing after natural disasters, reducing potential losses.
Landscaping businesses can use the identifier to deliver better services to their clients by assessing tree health on residential and commercial properties. This can streamline maintenance schedules and inform clients about necessary treatments or removals for unhealthy trees.
Companies aiming to enhance their sustainability efforts can use the tree health identifier to optimize their tree planting and maintenance initiatives. Healthy trees contribute to carbon offset programs, and using this function can help ensure that planted trees thrive.
Schools and universities can adopt this classification function in environmental science and ecology curricula. By integrating tree health monitoring into their programs, students can engage in hands-on learning experiences that promote awareness and responsibility towards urban and natural ecosystems.
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 a tree is healthy 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.