A pretrained how much weight someone is lifting classifier that sorts an image into one of 10 categories — how much weight someone is lifting. Use the how much weight someone is lifting 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 21 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": "10-15 Pounds",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 10 how much weight someone is lifting 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 could be used in fitness applications to analyze the weight being lifted by users during workouts. By accurately identifying lifting weights, trainers can provide personalized workout plans and adjustments to maximize effectiveness and prevent injuries.
Health clubs and gyms can implement this technology to monitor equipment usage in real-time. By understanding how much weight individuals are lifting, management can assess equipment efficiency, plan maintenance schedules, and identify popular equipment for better space allocation.
Coaches and sports analysts can leverage this function to measure and track athletes' lifting performance over time. It can help in determining training effectiveness, tailoring training regimens to individual athletes, and improving overall team performance through data-driven insights.
Physical therapists can use this identifier to track the progress of patients recovering from injuries. By accurately measuring the weights lifted during therapy sessions, therapists can adjust recovery plans and ensure that patients are progressing safely and effectively.
Fitness coaches can utilize this function to remotely monitor clients' weight lifting sessions through video analysis. This allows coaches to provide real-time feedback and correct form remotely, enhancing the coaching experience and supporting clients in achieving their fitness goals.
Fitness facilities can integrate this identifier in their systems to automatically generate reports on member performance and engagement. This data can help in refining marketing strategies, offering tailored programs, and enhancing customer retention by providing valuable insights.
Smart home gym equipment could incorporate this function to provide users with feedback on their workouts. By identifying how much weight someone is lifting, the system could recommend weight adjustments or exercises, creating a more interactive and personalized workout experience at home.
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 how much weight someone is lifting 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.