A pretrained arm vs leg muscle classifier that sorts an image into one of 2 categories. Use the arm vs leg muscle 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": "Arm Muscle",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 arm vs leg muscle 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 be utilized by sports coaches and trainers to assess the specific muscle development in athletes. By identifying the distribution of arm and leg muscles, trainers can tailor training programs to enhance performance based on weaknesses or imbalances.
Physical therapists can employ this image classification function to monitor patients recovering from injuries. By analyzing muscle strength in arms and legs, therapists can adjust rehabilitation protocols and track progress more effectively.
Fitness applications can integrate this classification feature to provide users with personalized workout recommendations. By understanding muscle conditions, users can receive targeted exercises that focus on building strength in either arms or legs.
Researchers in health and wellness can leverage this technology to study the correlation between muscle development and various health outcomes. By classifying muscle groups, they can gather insights that contribute to ongoing studies in sports science and physical health.
Clothing brands specializing in athletic wear can use this function to optimize fit and design based on muscle classification. By understanding the target demographic's physique, they can create products that cater to specific muscle group shapes and sizes, enhancing comfort and performance.
Manufacturers of smart fitness devices can incorporate this classification to enhance device calibration for individual users. By understanding a user's muscle makeup, devices can provide more accurate feedback related to calories burned or workout intensity tailored to arm and leg activities.
Personal trainers can utilize this function to create more effective and customized workout plans for their clients. By identifying muscle strengths and weaknesses, trainers can ensure balanced training that maximizes results and reduces the risk of injuries.
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 arm vs leg muscle 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.