Want to integrate your classifier into the AddaxAI model zoo?
The model integration is designed to be relatively developer-friendly. It expects classifiers to run on image crops generated by MegaDetector. If your model works differently (like using full images or a different input format) just let me know at peter@addaxdatascience.com.
Here’s what you need to know:
Inference
Each model has its own inference script that tells AddaxAI how to handle the model. I would like to know from:
- How to load your model
→ Example - How to crop an animal from a MegaDetector bounding box
→ Example - How to run inference on a
PIL.Imagecrop, including preprocessing
→ Example
You don’t need to integrate it into AddaxAI yourself. Just share a script or a few code snippets, that’s enough for me to take it from there.
Admin Info
Once your model works technically, we’ll finalize the release by creating a model card with metadata. Users will be able to download and run your model within AddaxAI.
Here’s what I need from you:
- Title
Short and descriptive—usually includes the region, model name/version, and developer.
Examples: “Europe - DeepFaune v1.1” or “Namibian Desert - Addax Data Science” - Description
What the model does and what it was trained on. - Developer
Your name or organization. - Owner (optional)
Shown in the model card if you want to name a separate owner. - Citation (optional)
URL to paper or publication to cite. - Information website (optional)
URL for users to learn more. - License (optional)
URL to the license information. Common choices:- CC BY-NC (non-commercial use, attribution required)
- Apache 2.0 (permissive, allows commercial use)
- MIT (very permissive)
- Default thresholds
Every model can define its own confidence thresholds. These will be editable by users but serve as defaults:- Detection threshold
The MegaDetector confidence level required to send a crop to the classifier.
Example: if set to 0.3, only detections above 0.3 are classified. - Classification threshold
The classification confidence required to accept a classification.
Example: if set to 0.8, predictions below 0.8 are labeled as “unidentified animal”.
- Detection threshold
Final Step
Once everything above is ready, let me know and I’ll handle the final integration: