ON THIS PAGE

  • HubAI
  • Overview
  • Model
  • Variant
  • Version
  • Instance
  • Model File(s)
  • Model Card

HubAI

Overview

HubAI is a currated collection of pre-trained models ready to be deployed in Luxonis ecosystem.

Model

Model is the basic element of HubAI, consisting of:
  • Model File(s) - Model file(s) or NN archive file(s) if model file(s) are packaged together with JSON metadata, and
  • Model Card - Comprehensive description of the model.
Models can be public or private (team-owned), and can freely be uploaded to or downloaded from the collection. For details, refer to the Upload/Download guide.Each Model File constitutes a separate Variant, characterized by its training configuration and usage context. To support efficient model development and precise referencing in deployed applications, we structure Model Files in three nested levels. This structure helps you:
  • Track your model's evolution,
  • Differentiate semantically distinct models,
  • And reference the correct artifact via the appropriate identifier.
Each nesting level — Variant, Version, and Instance — has its own unique ID, which can be used in your DepthAI pipeline to fetch the corresponding model from HubAI.The following diagram illustrates the hierarchical structure:
Markdown
1├── Variant 1
2|   ├── Version 1
3|   |   ├── Instance 1
4|   |   └── Instance 2
5|   └── Version 2
6|       ├── Instance 1
7|       └── Instance 2
8├── Variant 2
9...

Variant

A Variant is a specific version of a model, typically distinguished by input resolution (e.g., 224x224), dataset used for training (e.g., COCO), or some smaller variation in model architecture. Each variant is associated with a Variant ID.

Version

Each model Variant can have multiple Versions. Semantically, this can be used to track different weights of your model that you are developing. You can have an initial version, then a version finetuned, and a version that is, for example, used in production. Each Version has its Version ID.

Instance

An Instance is a compiled, platform-targeted version of a Variant — the actual artifact deployed to hardware. For more information about the conversion to a platform-specific instance, see the Conversion guideEach instance is identified by a unique Instance ID that points to a specific platform export rather than a general model variant/version. Therefore, when integrating an Instance into your pipeline, ensure the Instance ID corresponds to the correct platform for your deployment environment.

Model File(s)

Model file(s) (also referred to as model executable(s)) come standalone or packed into a NN Archive. The model file(s) must be either in ONNX (.onnx), OpenVINO IR (.xml and .bin), or TensorFlow Lite (.tflite) or one of the RVC compiled formats.

Model Card

A Model Card is a collection of information about the model. You can choose your own structure for private models. For public models, we suggest the following structure (check out the Model Card Template ):
Markdown
1# Model Details
2
3## Model Description
4... Description of the model functionality.
5
6- Developed By - Name of the model developer(s).
7- Shared By - Source of the model file/weights.
8- Model Type - General model type (e.g. computer vision).
9- License - Link to the license that governs the use/distribution of the model.
10- Resources - Link(s) to the model resouces (e.g. paper, sourecode, etc.)
11
12# Training Details
13
14## Training Data
15... Describe and link to the data used to train the model.
16
17# Testing Details
18
19## Metrics
20... Describe and report the metrics used to validate the model.
21
22# Technical Specifications
23
24## Input/Output Details
25... Name and a short description for each input/output tensor.
26
27## Model Architecture
28... Describe the model architecture (e.g. backbone, head, etc.).
29
30## Throughput
31... Report model throughput on RVC platform(s) for which the model is converted.
32
33## Quantization
34... Describe quantization data if quantization is used during conversion.
35
36# Utilization
37... Explain how to utilize the model in a DepthAI pipeline. Ideally provide or link to an example.