A pretrained if image is workplace safe classifier that sorts an image into one of 2 categories. Use the if image is workplace safe 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": "Workplace Safe",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if image is workplace safe 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 use case involves using the image classification function to automatically assess whether workplaces adhere to safety standards. By implementing this system, organizations can promptly identify potential hazards and ensure compliance with safety regulations, thereby reducing the risk of accidents.
Construction companies can utilize the image classification function to regularly evaluate job sites for safety violations. Automated assessments can help site managers quickly address unsafe conditions, thereby ensuring the well-being of workers and minimizing delays due to safety concerns.
The image classification function can be used to assess the safety of machinery and equipment in a workplace. By automating inspections, companies can maintain operational efficiency while ensuring that all equipment meets safety standards, which ultimately minimizes the risk of injuries.
Organizations can apply image classification to analyze training sessions capturing safety equipment and behaviors. This assessment can help measure the effectiveness of safety training programs, providing actionable insights to improve training methods and employee awareness.
Businesses can integrate the image classification function into their incident reporting systems to evaluate images from reported accidents. This analysis allows for quicker identification of systemic issues within the workplace, leading to improved safety protocols and preventive measures.
This use case focuses on the automatic classification of images showing the handling or storage of hazardous materials. By identifying unsafe practices in real time, organizations can enforce proper handling procedures and training, ultimately protecting employees and the environment.
Companies with remote or dispersed teams can use this image classification function to conduct virtual safety audits of various facilities. By collecting and analyzing images from different sites, auditors can evaluate safety conditions and provide recommendations without needing physical site visits, saving time and resources.
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 if image is workplace safe 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.