A pretrained leaf colors classifier that sorts an image into one of 10 categories — the color of the leaves.. Use the leaf colors 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 10 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": "Black",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 leaf colors 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.
This function can be employed to assess the health of crops by analyzing leaf colors. Discrepancies in expected leaf coloration may indicate nutrient deficiencies or diseases, allowing farmers to take timely action to improve crop yield.
Researchers can use the leaf color identification function to monitor changes in vegetation. Tracking shifts in leaf colors over time can provide insights into the impact of climate change on plant health and biodiversity in different ecosystems.
Garden centers and nurseries can utilize this technology for automated identification of plant species based on leaf color. This can assist customers in selecting the right plants for their gardens while providing tailored care advice for healthy growth.
In the food industry, this function can help inspect leafy greens and herbs during packing and shipping. By identifying color changes that signify spoilage or aging, companies can ensure that only the freshest products reach consumers.
Conservation organizations can leverage the leaf color classification to monitor endangered plant species. Identifying variations in leaf coloration might help in assessing the health of these species and the effectiveness of protection measures in place.
Educational platforms can implement this function in apps aimed at biology students. By allowing students to identify leaf colors and corresponding plant types, it enhances their hands-on learning experience regarding botany and ecological systems.
The fashion and design industries can utilize leaf color classification to inspire color palettes for textiles and products. By analyzing natural leaf colors, designers can create collections that reflect seasonal trends and harmonize with nature.
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 leaf colors 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.