PixelMind SDK

01Section

Lightweight Vision Inference Engine

Overview

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.

PythonPyTorchONNXDockerFlaskOpenCVLinuxGit

02Section

Tech Stack

03Section

Core Capabilities

Key Features

🔌

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.

04Section

Performance Metrics

Performance

Image Size: 10MB
+ FPS: 60
+ Platforms: 5
Latency: 8ms

05Section

Python Inference Example

Code 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%}")