A pretrained mango species classifier that sorts an image into one of 10 categories — what species of mango it is. Use the mango 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": "Alphonso",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 mango 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.
Researchers can utilize the mango species identifier to study the genetic diversity and characteristics of various mango species. By accurately classifying different species, they can identify traits for breeding programs aimed at improving mango quality and resilience.
Importers and exporters can employ this identification function to ensure that they are sourcing and distributing the correct mango species. This can streamline logistics and reduce costs by preventing mislabeling, thereby improving inventory management.
Food manufacturers and distributors can implement this classification function to ensure product consistency. By verifying the species of mangoes being used in their products, they can maintain quality and meet consumer expectations for taste and texture.
Retailers can use this technology to educate consumers about the different mango species available in the market. By providing specific identification on packaging, they can enhance customer knowledge and influence purchasing decisions based on flavor profiles and best uses.
Farmers can integrate this functionality to manage their crops more effectively by identifying the species of mangoes growing on their farms. This will aid in determining the appropriate care required, from pest management to harvesting time.
Online grocery platforms can leverage this classification tool to ensure that products are accurately labeled and categorized. This not only enhances the shopping experience but also helps consumers make informed decisions based on their preferences for specific mango species.
Conservation organizations can utilize the mango species identifier to monitor and protect rare and endangered mango varieties. By accurately identifying species in different ecosystems, they can develop targeted conservation strategies to safeguard biodiversity in tropical regions.
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 mango 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.