A pretrained screw type classifier that sorts an image into one of 10 categories — what type of screw it is. Use the screw 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 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": "Binder Screw",
"labelId": "label_...",
"confidence": 0.92
}No labeled training data behind this function — it picks between the 10 labels using a foundation model's general world knowledge (currently GPT-4o-mini). Your image is forwarded to the model provider at inference time. Because it's zero-shot, cloned label edits take effect immediately, no retraining needed.
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.
Implement a screw type identifier in production lines to automatically classify screws during assembly. This will help in detecting incorrect screw types, reducing errors and ensuring product quality is maintained throughout the manufacturing process.
Using the identifier in warehouses can streamline inventory processes by categorizing screw types automatically. This will help in tracking stock levels accurately and facilitate better organization of materials, leading to improved efficiency in order fulfillment.
E-commerce platforms can utilize the screw type identifier to enhance search accuracy for customers looking for specific screws. By improving the classification of products, customers can find the exact screws they need more quickly, enhancing the shopping experience.
In recycling facilities or industrial plants, the identifier can be integrated into sorting systems to distinguish between different types of screws. This will facilitate the recycling process by ensuring screws are sorted correctly, making it easier to reclaim materials.
Tool manufacturers can integrate the screw type identifier into their tools to alert users when using incorrect screws. By providing real-time feedback, users can avoid damaging materials or tools, leading to enhanced performance and durability.
Technicians in maintenance and repair can use mobile applications with the screw type identifier feature. This will assist them in quickly identifying the correct screws needed for repairs, thereby reducing time spent on troubleshooting and improving service efficiency.
In product design, engineers can utilize the screw type identifier to analyze and select the best screw types for their prototypes. This tool can streamline the design process by offering insights into the compatibility of screw types with different materials, leading to better product outcomes.
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 screw 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.