Overview
The Sylvester ML API analyses a photo of a cat and estimates whether the cat shows signs of comfort or discomfort.
You send one image. The API:
- Detects the cat in the image and returns where it is (a bounding box) and how confident the detector is.
- Classifies the detected cat as
comfortordiscomfortwith a probability score.
If no cat is found, or the detector isn't confident enough, the API tells you that instead of guessing.
Typical uses
- Veterinary clinic intake and triage tools
- Pet wellness and monitoring apps
- Telehealth consultations
- Insurance and shelter workflows that need a non-invasive signal
- At home consumer care
- At home pet hardware (e.g., smart litter boxes, feeders, cameras)
⚠️ The API gives a statistical pain signal to support human judgement. It is not a diagnosis. Sylvester.ai is not a diagnostic tool.
API versions at a glance
| v2 (recommended) | v1 (legacy) | |
|---|---|---|
| Endpoint | POST /v2/predict |
POST /invocations |
| Result labels | comfort / discomfort |
Happy / Unhappy (plus mood_class 0 / 1) |
| Score | Probability of discomfort (0–1), directly comparable to the threshold | Confidence in whichever class was predicted |
| "No cat" / low quality | HTTP 200 with a detection state |
HTTP 200 with success: false |
| Bad input | Real HTTP errors (400 / 422) with a consistent error body | Mostly HTTP 200 with success: false |
| Request tracing | X-Request-ID header + request_id in every response |
None |
| Public, machine-readable docs | Yes: OpenAPI at /v2/openapi.json |
No |
| Video | No (send frames as images) | Legacy endpoint only (deprecated) |
New integrations should use v2. v1 remains available for existing customers.
Placeholders in code samples
https://api.example-sylvester-host: replace with the production base URL.YOUR_API_KEY: replace with the key issued to your team.