PixelMind SDK

Overview

Lightweight Vision Inference Engine

PixelMind SDK is a vision model inference engine designed for edge devices and resource-constrained environments. Supporting PyTorch and ONNX inference backends with Docker containerization, it enables rapid deployment of trained vision models across diverse hardware platforms.

The SDK provides a unified Python API with built-in model format conversion (PyTorch → ONNX), supporting inference acceleration for YOLO, SAM and other mainstream vision models. Paired with Flask/FastAPI for quick inference service setup, achieving single-frame latency as low as 8ms.

Tech Stack

Python

PyTorch

ONNX

Docker

Flask

OpenCV

Linux

Git

Key Features

Core Capabilities

🔌

Multiple Backends

Unified Python API supporting both PyTorch and ONNX Runtime backends with automatic optimal backend selection based on hardware.

🐳

Docker Deployment

Built-in Dockerfile and docker-compose configuration for one-click inference service image building, supporting both GPU and CPU runtime modes.

🚀

Simple API

Three lines of code for model loading, inference, and result parsing. RESTful API interface for easy frontend-backend integration.

⚙️

Model Conversion

Built-in PyTorch → ONNX conversion tool supporting format conversion and validation for YOLO, SAM and other mainstream vision models.

Performance

Performance Metrics

10MB

Image Size

0

+ FPS

0

+ Platforms

8ms

Latency

Code Example

Python Inference Example

inference.py

from pixelmind import Engine
import cv2
# Initialize engine with ONNX backend
engine = Engine(
    model="yolov8n.onnx",
    backend="onnxruntime",
    device="cpu"
)
# Load image and run inference
image = cv2.imread("input.jpg")
results = engine.predict(image, conf=0.5)
for det in results:
    print(f"{det.label}: {det.score:.2%}")