Detect objects in images

How it works

What it covers

• Detect objects using YOLOX models (runs locally, no API needed)
• Get bounding boxes and class labels
• Filter detections by confidence threshold

What it uses

Install

terminal

$pip install pixeltable pixeltable-yolox

Pixeltable calls

yolox.yolox()pxt.create_table()@pxt.udfinsert()add_computed_column()

A look at the code

# Run YOLOX object detection
# model_id options: yolox_nano, yolox_tiny, yolox_s, yolox_m, yolox_l, yolox_x
images.add_computed_column(
    detections=yolox(images.image, model_id='yolox_m', threshold=0.5)
)

One cell of the notebook, copied as it is. Open it in Colab for the rest.

• Compare object detection and panoptic segmentation

  Understand when to use bounding boxes versus pixel-level masks for image analysis.

  Updated Apr 10, 2026Open in Colab

• Visualize object detections

  Draw bounding boxes on images to visualize object detection results.

  Updated Oct 13, 2026Open in Colab

• Promptable segmentation with SAM 3

  Segment every object matching a concept (a short text phrase or a bounding box) using Meta's Segment Anything Model 3.

  Updated Sep 25, 2026Open in Colab

• Generate captions for images

  Automatically create descriptive captions for images using AI vision models.

  Updated Apr 10, 2026Open in Colab