A pretrained outlet type classifier that sorts an image into one of 10 categories — what type of outlet it is. Use the outlet type 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": "Decorative",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 outlet type 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 help retailers identify the outlet types within their stores to optimize product placement and floor layouts. By understanding which areas are frequented by customers based on outlet types, retailers can strategically arrange products, thereby enhancing the shopping experience and boosting sales.
Marketers can leverage the outlet type identification to tailor their campaigns and promotions according to the specific characteristics of each outlet type. This information allows for more personalized marketing strategies that resonate with unique customer demographics, ultimately improving conversion rates.
Businesses can use the outlet type function to assess and predict inventory needs based on the typical stock turnover for each outlet type. This insight enables effective stock management and reduces excess inventory or stockouts, ultimately enhancing operational efficiency.
The function can facilitate performance tracking for different outlet types by providing insights into sales performance and customer engagement metrics. Analyzing sales data relative to outlet types helps businesses identify best-performing locations and make informed operational decisions.
Organizations can utilize outlet type classification to benchmark their performance against competitors in similar outlet types. This comparative analysis helps businesses identify strengths and weaknesses, allowing them to adjust strategies to gain competitive advantages.
The outlet type identifier can inform business decisions regarding geographic expansion by pinpointing areas with a high density of target outlet types. Understanding the outlet landscape helps businesses strategically choose locations for new stores, enhancing the likelihood of success.
By analyzing consumer interactions with different outlet types, companies can gain deeper insights into shopping behavior and preferences. This understanding allows them to tailor their services and product offerings to better align with consumer needs, ultimately fostering loyalty and increasing customer satisfaction.
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 outlet type 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.