Pretrained computer vision classifier
Identify bolt vs screw with one API call.
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.
Try the bolt vs screw classifier
Drop in a photo and get the prediction back. No signup, no setup.
What this bolt vs screw classifier recognizes
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.
Call the bolt vs screw API
Get your own copy of this classifier behind your own endpoint — callable from any HTTP client:
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "https://example.com/photo.jpg"}'import requests
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer YOUR_ACCESS_TOKEN"},
json={"data": "https://example.com/photo.jpg"},
)
print(response.json())const response = await fetch("https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke", {
method: "POST",
headers: {
"Authorization": "Bearer YOUR_ACCESS_TOKEN",
"Content-Type": "application/json",
},
body: JSON.stringify({ data: "https://example.com/photo.jpg" }),
});
console.log(await response.json());$ch = curl_init();
curl_setopt($ch, CURLOPT_URL, 'https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke');
curl_setopt($ch, CURLOPT_RETURNTRANSFER, 1);
curl_setopt($ch, CURLOPT_POST, 1);
curl_setopt($ch, CURLOPT_POSTFIELDS, '{"data": "https://example.com/photo.jpg"}');
$headers = array();
$headers[] = 'Authorization: Bearer YOUR_ACCESS_TOKEN';
$headers[] = 'Content-Type: application/json';
curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
$result = curl_exec($ch);
curl_close($ch);
echo $result;Example response
{
"labelName": "Bolt",
"labelId": "label_...",
"confidence": 0.92
}Under the hood
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.
Where teams use bolt vs screw classification
Automated Quality Control
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.
Inventory Management
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.
E-commerce Product Verification
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 Toolkits
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.
Robotic Assembly Systems
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.
Training and Education
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.
DIY Suggestion Apps
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.
Common questions
What's the difference between a zero-shot and a Nyckel-trained classifier?
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.
How do I know whether this will work for my application?
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.
What happens when it makes a mistake?
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.
Do I need training data to get started?
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.
What does it cost to try?
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.
Related classifiers
Ready to classify bolt vs screw at scale?
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.