Introduction to PixelFlow
PixelFlow is an open-source Python library that provides a unified interface for working with computer vision models. It solves the fundamental problem of framework fragmentation by standardizing detection outputs, annotations, and processing pipelines and more.
Simple Computer Vision API
import pixelflow as pffrom ultralytics import YOLO# Any YOLO model worksmodel = YOLO('yolov8n.pt')results = model('image.jpg')# Convert to PixelFlow formatdetections = pf.from_ultralytics(results[0])# Professional annotations in one lineannotated = pf.annotate.box(image, detections)The Problem
Computer vision development faces a persistent challenge: every framework outputs data differently. This fragmentation means:
- Code written for one framework doesn't work with another
- Switching models requires rewriting processing pipelines
- Teams maintain duplicate code for identical functionality
- Testing across frameworks becomes complex
PixelFlow eliminates this by providing a single, consistent API that works identically regardless of your underlying model.
Framework Compatibility
PixelFlow provides unified converters for all major computer vision frameworks, eliminating vendor lock-in and enabling framework mixing within the same project.
Supported Frameworks:
- YOLO/Ultralytics - Real-time object detection
- Detectron2 - Research-grade instance segmentation
- HuggingFace Transformers - Vision transformers
- Custom Models - Build your own converters