Iris: Computer Vision Model Training

SUMMARY

Iris (telekinesis-iris) is the computer vision model training package within the Telekinesis Agentic Skill Library. It provides a unified framework for preparing datasets, training and fine-tuning models, evaluating performance, and exporting trained models for downstream inference and deployment.

Install Telekinesis Skill Library

Set up your Python environment, configure your Telekinesis API key, and install the Skill Library before getting started with Iris.

Open installation →

Explore the Telekinesis Quickstart

Get familiar with the Skill Library through guided examples covering computer vision, perception, and robotics workflows.

Open quickstart →

Iris Training Workflow

Iris standardizes the model training lifecycle across supported architectures. Prepare a COCO dataset, configure a trainable model, execute training and evaluation, and export the selected checkpoint for downstream inference and deployment.

1 · Prepare data

COCODataset

loads images, boxes, class labels, and optional masks from COCO annotations

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2 · Configure the model

TrainableModel

provides the model, preprocessing, loss, evaluation, and export behavior

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3 · Train and evaluate

Trainer

runs training and validation, reports metrics, and saves resumable checkpoints

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4 · Export

export_model

converts the selected checkpoint into a deployment-ready artifact

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5 · Deploy

deploy.Model

loads the exported artifact behind a model-family-neutral inference API

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6 · Predict

Prediction

returns boxes, scores, class IDs, optional class names, and optional masks

When to Use Iris?

Use Iris when you need to train or fine-tune computer vision models on custom datasets for application-specific tasks.

Typical use cases include:

• Object detection: Train models to recognize and localize custom object classes.

• Instance segmentation: Train models to identify individual objects and generate pixel-level masks.

• Domain adaptation: Fine-tune pretrained models for new objects, environments, and imaging conditions.

• Model evaluation: Track training performance and evaluate models using standardized metrics.

• Model export: Prepare trained models for inference and integration into downstream applications.

What Does Iris Provide?

Iris provides a shared dataset, training, evaluation, checkpointing, export, and inference workflow.

Supported Models

Iris currently supports the following model families:

Model	Training Tasks	Export Format
RF-DETR	Object Detection · Instance Segmentation	ONNX (.onnx)
SAM3-LoRA	Prompt-driven Instance Segmentation · LoRA Fine-tuning	Iris Bundle (.pt)

Guides

Prepare a Dataset

Load COCO detection and segmentation datasets, including Roboflow-style splits.

Prepare data →

Train and Resume a Model

Train RF-DETR or SAM3-LoRA and continue runs from saved checkpoints.

Start training →

Export and Deploy

Export a training checkpoint and run model-family-neutral inference.

Deploy a model →

Configuration

Reference the shared trainer, model, export, and post-processing settings.

View options →