A pretrained bolt vs screw classifier that sorts an image into one of 2 categories. Use the bolt vs screw 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 2 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": "Bolt",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 bolt vs screw 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.
Manufacturing plants can utilize the bolt vs screw identifier to automate quality control processes. By integrating the classifier into production lines, factories can ensure that only the correct fasteners are used, reducing the risk of assembly errors and improving product quality.
Warehouse and inventory management systems can incorporate the identifier to streamline sorting and organization of fasteners. By quickly distinguishing between bolts and screws, businesses can enhance their inventory accuracy and reduce search times for specific components.
Online retailers can use the classification technology to automatically verify product listings. By confirming that images of fasteners match their descriptions as bolts or screws, businesses can improve customer trust and reduce return rates due to incorrect shipments.
Augmented reality applications for DIY enthusiasts can implement this identifier to assist users in selecting the right fasteners for their projects. By recognizing and labeling bolts and screws, the app can provide instant recommendations, helping users avoid costly mistakes in their repairs or constructions.
In robotic assembly applications, using a bolt vs screw identifier can enable robots to select and apply the correct fastener during assembly processes. This capability can enhance the efficiency and accuracy of automated systems in various industries, from automotive to electronics.
Educational institutions or training programs in mechanical engineering can utilize the classifier in teaching environments. Students can engage with hands-on learning regarding different fasteners while leveraging the classification tool to understand their applications and functions better.
Mobile applications designed for home improvement projects can integrate the identifier to provide users with precise advice on which fasteners to use. By analyzing user-uploaded images, the app can suggest suitable bolts or screws based on visual recognition, thus enhancing the user experience in DIY projects.
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 bolt vs screw 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.