A pretrained squash species classifier that sorts an image into one of 10 categories — what species of squash it is. Use the squash species 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 20 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": "Acorn Squash",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 squash species 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 squash species identifier can be integrated into agricultural quality control systems. This function helps farmers and producers determine the species of squash being harvested, ensuring only top-quality produce is sent to market, thereby maximizing customer satisfaction and reducing waste.
Retailers can utilize the species identifier to analyze the diversity of squash sold in their stores. By understanding which species are most popular among consumers, they can tailor their stock to meet demand and improve sales strategies.
Researchers studying squash genetics can employ the identifier to quickly classify various squash species in their studies. This can accelerate the pace of research by allowing for accurate data collection and analysis regarding species-specific traits and growth patterns.
Distributors can implement the squash species identifier to streamline their supply chain operations. By accurately identifying the species during distribution, they can better manage inventory levels, reduce spoilage, and enhance logistical efficiency.
Educational institutions can utilize the squash species identifier to teach students about agricultural science and botany. This hands-on tool can help students learn how to identify different squash species and understand their specific growing conditions and nutritional profiles.
Organic farms can use the identifier to ensure they are cultivating and promoting a diversity of squash species. This can enhance biodiversity, improve soil health, and encourage ecological resilience within their farming systems.
Developers can create mobile applications that allow consumers to identify squash species at the point of purchase or during cooking. Such an app could enhance user engagement by providing recipes and nutritional information tailored to the specific species identified.
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 squash species 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.