A pretrained tree leaf shapes classifier that sorts an image into one of 10 categories — what type of tree leaf shape it is. Use the tree leaf shapes 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": "Acuminate",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 tree leaf shapes 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.
The tree leaf shapes identifier can assist farmers and agricultural professionals in monitoring crop health by analyzing the shapes of tree leaves. Identifying sick or unhealthy leaves can lead to prompt interventions, maximizing yield and reducing losses.
Researchers can utilize this identifier in ecological studies to classify and catalog different tree species based on their leaf shapes. This can aid in biodiversity assessments and help in understanding evolutionary relationships among various plant species.
Conservationists can use the tree leaf shapes identifier to identify protected or endangered tree species in various ecosystems. This information can inform conservation strategies and help in tracking changes in forest composition due to climate change or human activities.
Educational institutions can implement the leaf shape identifier as a teaching tool in botany and environmental science courses. It can provide students with interactive learning opportunities, allowing them to better understand plant identification and taxonomy.
Forestry managers can leverage this technology to efficiently inventory tree species in specific areas and assess forest health. It enables better planning for logging activities, reforestation efforts, and habitat protection by providing accurate species distribution data.
Urban planners can utilize the identifier to assess the types of trees in urban areas for aesthetic, ecological, and infrastructural planning. By understanding which tree species are present, they can make informed decisions regarding landscaping, shade provision, and urban biodiversity.
Companies focused on agricultural technology can incorporate the leaf shape classification function into their products, such as pest detection systems or nutrient management tools. This technology can enhance precision agriculture by providing insights on how specific tree species react to different agricultural practices.
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 leaf shapes 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.