A pretrained battleship coordinates classifier that sorts an image into one of 10 categories — the position of enemy ships on the grid. Use the battleship coordinates 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 25 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": "Ambush",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 battleship coordinates 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 assist game developers in identifying whether the AI-controlled components in a digital battleship game are accurately processing the coordinates provided for hits and misses. By validating the classification of coordinates, developers can troubleshoot inconsistencies and enhance game mechanics.
In educational settings, this function can be used to create an engaging learning experience for students studying basic programming and algorithms. By applying the battleship coordinates identifier, students can learn about conditional logic and decision-making through a familiar game format.
When developing machine learning models for strategic games, this function can be utilized to label training data effectively. By ensuring that the model is trained on accurate coordinate classifications, developers can enhance the performance and reliability of AI systems used in gaming applications.
This identifier can be integrated into competitive gaming platforms to check the integrity of games and prevent cheating. By analyzing the coordinates of player moves, the system can identify suspicious patterns that might indicate cheating tactics, thus maintaining fairness in competition.
Game developers can use this function to analyze player behaviors by examining the success rates of hits and misses based on coordinate classifications. This data can lead to insights into player strategies and inform future game design or balancing adjustments.
In military training simulations, this function could classify simulated attack coordinates in tactical exercises. By determining the accuracy of different coordinate responses, trainers can provide feedback and improve strategy among military personnel.
Businesses can implement this function in marketing campaigns that utilize gamified experiences surrounding naval combat themes. By tracking players' input on battleship coordinates, companies can engage customers through interactive content that enhances brand visibility while collecting data on user interactions.
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 battleship coordinates 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.