Intermediate · 30 min

Computer Vision Pipeline: Object Detection, Classification, and Search

Build optimized computer vision workflows with Pixeltable. Run YOLOX, CLIP, and custom models as computed columns with automatic batching, caching, and incremental processing.

Quick Start View on GitHub Documentation

The Challenge

Computer vision pipelines require managing image preprocessing, running multiple models (detection, classification, embedding), storing results, and keeping everything in sync. Adding a new model or changing thresholds means reprocessing millions of images manually.

The Solution

Pixeltable makes CV pipelines declarative. Define models as computed columns, and Pixeltable handles execution, batching, caching, and incremental updates. Change a threshold and only affected results are recomputed.

Implementation Guide

Step-by-step walkthrough with code examples

Step 1 of 4

Image Table

Create a table for images with metadata tracking.

python

1import pixeltable as pxt
2
3# Create image processing pipeline
4images = pxt.create_table('app.images', {
5    'image': pxt.Image,
6    'source': pxt.String,
7    'timestamp': pxt.Timestamp,
8    'camera_id': pxt.String,
9})
10
11# Insert images: local, S3, or URL
12images.insert([
13    {'image': 's3://bucket/cam01/frame_001.jpg',
14     'source': 'warehouse', 'camera_id': 'cam-01'},
15    {'image': '/data/inspection/part_42.png',
16     'source': 'qc-station', 'camera_id': 'cam-02'},
17])

Images are stored as native types with full metadata. No separate image registry or file management.

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Key Benefits

10x faster CV pipeline development vs custom code

Automatic batching and GPU utilization

Built-in result caching eliminates redundant inference

Incremental processing: only new images trigger computation

Mix vendor models (YOLOX, CLIP) with custom PyTorch models seamlessly

Real Applications

Manufacturing quality control and defect detection

Retail inventory monitoring and shelf analytics

Security and surveillance image analysis

Medical image classification and triage

Autonomous vehicle perception data processing

Prerequisites

Familiarity with computer vision concepts

Python and basic ML experience

Python 3.9+

GPU recommended for large-scale inference

PyTorch for custom model support

Performance

Development Speed

vs building custom CV infrastructure

10x faster

Compute Savings

With caching and incremental processing

70%

Learn More

YOLOX Object Detection in Videos

Detailed YOLOX integration guide

CV Data Curation & Annotation

FiftyOne and Label Studio integration

Accelerating Multimodal Annotations

Speed up annotation workflows with AI

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Ready to get started?

Install Pixeltable and start building in minutes. One pip install, no infrastructure to manage.

View on GitHub Quick Start Guide Starter Kit