A pretrained streaming video resolution classifier that sorts an image into one of 6 categories — the optimal streaming video resolution based on viewer conditions. Use the streaming video resolution 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 6 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": "1080P",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 6 streaming video resolution 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 focuses on enhancing the streaming experience by automatically adjusting video resolution based on network conditions and user device capabilities. By identifying false classifications of video quality, the system can ensure that users receive optimal playback without buffering or interruptions.
This function can be utilized to monitor live streaming events, identifying false claims of high-resolution content. Prompt alerts can help operators quickly address technical issues that could affect viewer experience, ensuring seamless broadcasts.
By analyzing viewer behavior regarding streaming resolutions that are falsely attributed, businesses can gain insights into audience preferences. This data can inform content strategy, helping producers create videos that align better with audience demands.
This use case involves employing the identifier to conduct routine checks on video uploads to platforms. By ensuring that false classifications are minimized, businesses can maintain a standard of quality and avoid user dissatisfaction from misrepresented content.
The function can help enhance adaptive streaming technologies by refining algorithms that estimate content resolution. This leads to a more tailored user experience across different devices and connection speeds, ultimately boosting user retention.
This use case is centered on preventing fraudulent claims about video quality in promotional materials or advertisements. By verifying streaming resolutions, businesses can avoid misleading marketing practices that could damage their reputation and trust with consumers.
By accurately identifying false classifications, organizations can optimize their bandwidth usage for video content delivery. This can lead to significant cost savings, as resources can be allocated more efficiently, especially during peak traffic times.
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 streaming video resolution 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.