Telekinesis Agentic Skill Library

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Retina: Object Detection Skills ​

SUMMARY

Retina is a module in the Telekinesis SDK containing skills for 2D object detection from images.

It provides detection capabilities using classical computer vision techniques, deep learning models, and modern foundation models, enabling reliable visual perception across a wide range of robotics applications.

Install Telekinesis Skill Library
Create an API key, install telekinesis-ai and module specific installations and dependencies in a Python environment.
Open installation →
Go to the Quickstart
Pick a starter and run your first Skill end-to-end - 2D / 3D vision, webcam capture, robot motion, or a full vision-to-robot pipeline, all visualized in Rerun.
Open quickstart →

When to Use Retina? ​

Use Retina when your robotics application relies on 2D visual detection from cameras, such as:

  • Object detection for vision-guided manipulation
  • Visual target identification for pick-and-place pipelines
  • Perception for mobile robot navigation and obstacle awareness
  • Visual inspection and monitoring in robotic systems
  • Camera-based input for Physical AI agents

What Does Retina Provide? ​

Retina includes a collection of modular skills for:

  • 2D object detection using classical vision algorithms
  • Deep learning–based object detectors
  • Foundation model–based and prompt-driven detection
  • Image preprocessing and feature extraction for detection pipelines

How to Use Retina? ​

To use the skills from Retina, simply use:

python
from telekinesis import retina

Here is a minimal example:

python
from telekinesis import retina, datatypes

# ===================== Load Image ==========================================
image_url = "https://assets.telekinesis.ai/examples/v1/images/metal_gears.jpg"
image = datatypes.Image.from_url(url=image_url).to_grayscale()

# ===================== Run Skill ==========================================
circles = retina.detect_circle_using_classic_hough(
  image=image,
  inverse_resolution_ratio=1,
  min_distance=50,
  min_radius=40,
  max_radius=60,
  canny_detector_upper_threshold=300,
  accumulator_threshold=30,
)

Overview of Skills ​

6 skills

Detect Circle Using Classic HoughRetina

Detect Circle Using Classic Hough

Detect circular objects in grayscale images using the classic Hough Circle Transform, returning circle centers and radii for geometric analysis.

Detect ContoursRetina

Detect Contours

Extract object outlines from binary or high-contrast images using contour detection for shape analysis and measurement.

Detect Objects Using RF-DETRRetina

Detect Objects Using RF-DETR

Detect objects in images using RF-DETR with COCO 80-class categories, returning COCO-like detection results.

Detect Objects Using QWENRetina

Detect Objects Using QWEN

Perform flexible, name-driven object detection in images using the QWEN Vision Language Model, returning COCO-like detection results with categories.

Detect Objects Using YOLOXRetina

Detect Objects Using YOLOX

Detect objects in images using YOLOX with COCO 80-class categories, returning COCO-like detection results.

Detect Objects Using Grounding DINORetina

Detect Objects Using Grounding DINO

Perform open-vocabulary object detection using Grounding DINO with a list of target object names, returning COCO-like detection results.