A pretrained how many cars are in the parking lot classifier that sorts an image into one of 10 categories — how many cars are present in the parking lot. Use the how many cars are in the parking lot 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 16 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": "0 Cars",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 how many cars are in the parking lot 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 integrated into parking management systems to provide real-time data on vehicle occupancy. It allows managers to optimize space allocation and notify drivers of available spots, enhancing overall efficiency.
City planners can use this classification function to analyze parking trends in urban areas. By understanding how many cars are parked at different times, they can make informed decisions on infrastructure improvements and traffic flow management.
Event organizers can employ this technology to monitor parking availability during large events. By assessing the number of cars, they can implement strategies to manage overflow and ensure smooth attendee experiences.
This function can help monitor parking utilization at electric vehicle (EV) charging stations. By determining how many cars are parked, operators can better manage charging availability and encourage the efficient use of EV infrastructure.
Retail businesses can use this image classification to streamline their parking facilities. By analyzing parking data, they can adjust marketing strategies and promotions based on peak parking times to maximize customer footfall.
Security teams can leverage this function to ensure that parking lots are being used safely and efficiently. By keeping track of vehicle counts, they can identify any unusual patterns that need further investigation.
Public transportation agencies can integrate this function to evaluate parking lot usage at transit hubs. Understanding car counts can help them plan transit schedules and offer more effective shuttle services based on parking demand.
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 how many cars are in the parking lot 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.