A pretrained fruits by texture classifier that sorts an image into one of 10 categories — what type of fruit it is based on its texture. Use the fruits by texture 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 15 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": "Bumpy",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 fruits by texture 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 'fruits by texture' identifier can be used in food processing facilities to automatically assess the quality of fruits based on their texture. This ensures that only fruits meeting the required quality standards are processed further, reducing waste and enhancing product consistency.
Grocery stores can implement this function to categorize and manage fruit inventory based on texture-related characteristics. By optimizing display strategies and stock levels, stores can enhance customer experiences and increase sales of textured fruits.
Meal kit delivery services can leverage the texture identification function to ensure that customers receive fruits that meet specific texture preferences. This personalization can improve customer satisfaction and reduce return rates for unsatisfactory produce.
Researchers studying fruit development and texture can use this function to categorize and analyze different varieties based on their texture profiles. This data can facilitate studies on texture-related traits and breed development for improved fruit quality.
Manufacturers of smart kitchen devices can integrate this texture identification technology to provide users with guidance on fruit ripeness and best use cases in recipes. This could enhance cooking experiences by suggesting optimal fruit usage based on texture.
Mobile applications focused on health and nutrition can utilize the texture identifier to suggest fruits that align with specific dietary needs or preferences. This feature can help users select fruits that provide the desired mouthfeel and nutritional benefits.
The texture classification function can assist in monitoring the safety of fruits by identifying abnormalities that may indicate spoilage or contamination. This proactive approach helps in maintaining food safety standards and preventing foodborne illnesses.
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 fruits by texture 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.