A pretrained screwdriver type classifier that sorts an image into one of 10 categories — what type of screwdriver it is. Use the screwdriver 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 15 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": "Clutch",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 screwdriver type 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 automatically classify various screwdriver types in a warehouse or tool store, improving inventory accuracy. By integrating with inventory management systems, it can provide real-time updates on stock levels and streamline ordering processes.
In a manufacturing setting, this function can be utilized on the production line to identify the type of screwdrivers being used. This ensures that the correct tools are utilized for specific tasks, reducing mistakes and enhancing product quality.
The function can analyze the types of screwdrivers used in industrial applications, providing insights for predictive maintenance. By identifying wear patterns, companies can schedule tool replacements or repairs before failures occur, minimizing downtime.
Customer service platforms can use this identification function to guide users on the right screwdriver type needed for product repairs. This not only enhances user satisfaction but also reduces the number of incorrect tool purchases or repair attempts.
Online retailers can implement this identification feature to recommend compatible screwdriver types when customers are viewing related products. This can boost sales by helping users find the right tools for their projects, enhancing the overall shopping experience.
The function can be incorporated into training programs for new employees in automotive or construction settings, teaching them about various screwdriver types and their uses. This helps enhance safety and operational efficiency by ensuring workers are familiar with the correct tools.
Mobile applications focused on home improvement can leverage this function to help users identify and select the appropriate screwdriver types for their tasks. By providing this functionality, apps can enhance user engagement and promote confidence in DIY home 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 screwdriver 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.