MaixPy (v4)

Let's Sipeed up, Maximize AI's power!

MaixPy (v4): Easily create AI projects with Python on edge device

Quick Start | Documentation | API | Hardware

GitHub Repo stars Apache 2.0 PyPI PyPI - Downloads GitHub repo size Build MaixCAM Trigger wiki

English | 中文

MaixCAM

Feature Overview

MaixPy offers simple Python programming combined with powerful edge computing hardware. Integrated hardware peripheral operations, video streaming, AI vision algorithms, audio algorithms, and LLM / VLM etc. With its plug-and-play design, MaixPy enables you to quickly implement your intelligent projects.

Additionally, MaixPy provides the MaixVision IDE, MaixHub online training platform, detailed documentation, and even a C/C++ SDK with identical APIs, ensuring seamless development and production deployment.

Here is a partial video demonstration of the features. For more documentation, please visit the official website: wiki.sipeed.com/maixpy/

If you like this project, please click Star on the top right of the MaixPy Project to encourage us to develop more exciting content!

Concise and Efficient Code (API) Design

With MaixPy you can easily create AI vision project within 10 lines of code:

from maix import camera, display, image, nn

classifier = nn.Classifier(model="/root/models/mobilenetv2.mud")
cam = camera.Camera(classifier.input_width(), classifier.input_height(), classifier.input_format())
disp = display.Display()

while 1:
    img = cam.read()
    res = classifier.classify(img)
    max_idx, max_prob = res[0]
    msg = f"{max_prob:5.2f}: {classifier.labels[max_idx]}"
    img.draw_string(10, 10, msg, image.COLOR_RED)
    disp.show(img)

Result:

Edge(embeded) friendly

Simply use hardware peripheral like serial port:

from maix import uart

devices = uart.list_devices()

serial = uart.UART(devices[0], 115200)
serial.write_str("hello world")
print("received:", serial.read(timeout = 2000))

MaixVision workstation

We also provide a handy MaixVision workstation software to make development easier and faster:

MaixVision

MaixHub online platform

MaixHub provide free online AI train service, one click to train AI model and deploy to MaixCAM even you have no AI knowledge and expensive training equipment.

MaixHub

Hardware platform MaixCAM

And we provide two powerful hardware platform MaixCAM2, MaixCAM and MaixCAM-Pro.

MaixCAM

Performance comparison

K210 and v831 are outdated, they have many limitations in memory, performance, NPU operators missing etc.
No matter you are using them or new comer, it's recommended to upgrade to MaixCAM and MaixPy v4.

Here's the comparison between them:

Feature Maix-I K210 MaixCAM MaixCAM2
CPU 400MHz RISC-V x2 1GHz RISC-V(Linux)
700MHz RISC-V(RTOS)
25~300MHz 8051(Low Power)		 1.2GHz A53 x2(Linux)
RISC-V 32bit E907(RTT)
Memory 6MB SRAM 256MB DDR3 1GB / 4GB LPDDR4
NPU 0.25Tops@INT8
official says 1T but...		 1Tops@INT8 3.2Tops@INT8
Encoder ❌ 2880x1620@30fps H.254/H.265/JPEG 3840*2160@30fps H.254/H.265/JPEG
Decoder ❌ 2880x1620@30fps H.264/JPEG 1080p@60fps H.264/JPEG
Screen 2.4" 320x240 2.3" 552x368(MaixCAM)
2.4" 640x480(MaixCAM-Pro)
5" 1280x720
7" 1280x800
10“ 1280x800		 2.4" 640x480
5" 1280x720
7" 1280x800
10“ 1280x800
Touchscree ❌ 2.3" 552x368/2.4" 640x480 2.4" 640x480
Camera 30W 500W(5M) 800W(8M)
AI ISP ❌ ❌ ✅
WiFi 2.4G WiFi6 2.4G/5G WiFi6 2.4G/5G
BLE ❌ BLE5.4 BLE5.4
USB ❌ USB2.0 USB2.0
Ethernet ❌ 100M(Optional) 100M(on board FPC, can convert to RJ45 module)
SD Card SPI SDIO SDIO
OS RTOS Linux(BuildRoot) + RTOS Linux(Ubuntu) + RTT
Porgraming Language C / C++ / MicroPython C / C++ / Python3 C / C++ / Python3
SDK MaixPy-v1 MaixCDK + MaixPy v4
+ opencv + numpy + ...
Pure Python package or cross-compile manually		 MaixCDK + MaixPy v4
+ opencv + numpy + scipy + ...
Many AArch64 pre-compiled packages, and support compile on board
PC Software MaixPy IDE MaixVision Workstation MaixVision Workstation
Documentation ⭐️⭐️⭐️⭐️ 🌟🌟🌟🌟🌟 🌟🌟🌟🌟🌟
Online AI train ⭐️⭐️⭐️ 🌟🌟🌟🌟🌟 🌟🌟🌟🌟🌟
Official APPs ⭐️ 🌟🌟🌟🌟🌟 🌟🌟🌟🌟🌟
Ease of use ⭐️⭐️⭐️⭐️ 🌟🌟🌟🌟🌟 🌟🌟🌟🌟🌟
AI classify(224x224) MobileNetv1 50fps
MobileNetv2 ❌
Resnet ❌		 MobileNetv2 130fps
Resnet18 62fps
Resnet50 28fps		 MobileNetv2 1218fps
Resnet50 200fps
AI detect
only forward part /
[include pre-post process parts(Python)] /
[dual buff mode(Python)]
YOLOv2:
224x224: 15fps
YOLOv5s:
224x224: 100fps
320x256 70fps
640x640: 15fps
YOLOv8n:
640x640: 23fps
YOLO11n:
224x224: 175fps
320x224: 120fps
320x320: 95fps
640x640: 23fps
YOLOv5s:
224x224: 495fps
320x256: 400fps
640x480: 106fps / 73fps / 103fps
640x640: 80fps
YOLO11n:
224x224: 1214fps
640x480: 168fps / 77fps / 143fps
640x640: 113fps / 56fps / 98fps
YOLO11s:
640x480: 87fps / 53fps / 83fps
640x640: 62fps / 39fps / 59fps
YOLO11l:
640x640: 19fps / 16fps / 19fps
LLM ❌ ❌ Qwen/DeepSeek 0.5B(fftf: 640ms, 9 tokens/s)
Qwen/DeepSeek 1.5B(fftf: 1610ms, 4 tokens/s)
VLM(InterVL 1B)
Mode models
OpenMV algorithms
test image refer to Benchmark APP
test image refer to Benchmark APP
test date: 2025.8.22，update may have optimization
test image refer to Benchmark APP
test date: 2025.8.22，update may have optimization
Binary
 Gray 320x240: 7.4ms (135fps)
Gray 640x480: ❌
RGB 320x240: 11.3ms (88.5fps)
RGB 640x480: ❌		 Gray 320x240: 3.1ms (326fps)
Gray 640x480: 11ms (90fps)
RGB 320x240: 13.2ms (75fps)
RGB 640x480: 52.8ms (18fps)		 Gray 320x240: 1.3ms (799fps)
Gray 640x480: 4.8ms (206fps)
RGB 320x240: 3.4ms (294fps)
RGB 640x480: 13.3ms (75fps)
Find blobs
 320x240: 8.8ms (114fps)
640x480: ❌		 320x240: 7ms (143fps)
640x480: 20ms (50fps)		 320x240: 3.7ms (271fps)
640x480: 11.1ms (89fps)
1channel histogram
 320x240: 7.7ms (130fps)
640x480: ❌		 320x240: 10.9ms (91fps)
640x480: 42.8ms (23fps)		 320x240: 1.5ms (661fps)
640x480: 5.9ms (168fps)
QR Code
 320x240: 130.8ms (7.6fps)
640x480: ❌		 640x480: 136.9ms (7fps)
NPU acceleration：
  320x240: 22.1ms (45fps)
  640x480: 57.6ms (17fps)		 640x480: 57.9ms (17fps)
NPU acceleration：
  320x240: 9.2ms (109fps)
  640x480: 23.2ms (43fps)
OpenCV algorithms
test image refer to Benchmark APP
test date: 2025.8.22，update may have optimization
test image refer to Benchmark APP
test date: 2025.8.22，update may have optimization
Binary
 ❌ Gray 320x240: 2.2ms (463fps)
Gray 640x480: 7.1ms (140fps)		 Gray 320x240: 0.1ms (8174fps)
Gray 640x480: 0.3ms (2959fps)
Gray adaptive binary
 ❌ 320x240: 5.8ms (171fps)
640x480: 21.3ms (46fps)		 320x240: 1.6ms (608fps)
640x480: 6.3ms (159fps)
1channel histogram
 ❌ 320x240: 1ms (1000fps)
640x480: 6.2ms (160fps)		 320x240: 0.4ms (2308fps)
640x480: 1.7ms (604fps)
Find Contours
 ❌ 320x240: 2.8ms (351fps)
640x480: 8.6ms (116fps)		 320x240: 0.4ms (2286fps)
640x480: 1.4ms (692fps)

Maix Ecosystem

MaixPy not only a Python SDK, but have a whole ecosystem, including hardware, software, tools, docs, even cloud platform etc. See the picture below:

Who are using MaixPy?

  • AI Algorithm Engineer who want to deploy your AI model to embedded devices.

MaixPy provide easy-to-use API to access NPU, and docs to help you develop your AI model.

  • STEM teacher who wants to teach AI and embedded devices to students.

MaixPy provide easy-to-use API, PC tools, online AI train service ... Let you focus on teaching AI, not the hardware and complicated software usage.

  • Maker who want to make some cool projects but don't want to learn complicated hardware and software.

MaixPy provide Python API, so all you need is learn basic Python syntax, and MaixPy's API is so easy to use, you can make your project even in a few minutes.

  • Engineer who want to make some projects but want a prototype as soon as possible.

MaixPy is easy to build projects, and provide corresponding C++ SDK, so you can directly use MaixPy to deploy or transfer Python code to C++ in a few minutes.

  • Students who want to learn AI, embedded development.

We provide many docs and tutorials, and lot of open source code, to help you find learning route, and grow up step by step. From simple Python programming to Vision, AI, Audio, Linux, RTOS etc.

  • Enterprise who want to develop AI vision products but have no time or engineers to develop complicated embedded system.

Use MaixPy even graphic programming to develop your products with no more employees and time. For example, add a AI QA system to your production line, or add a AI security monitor to your office as your demand.

  • Contestants who want to win the competition.

MaixPy integrate many functions and easy to use, fasten your work to win the competition in limited time. There are already many contestants win the competition with MaixPy.

Compile Source Code

If you want to compile MaixPy firmware from source code, refer to Build MaixPy source code page.

License

All files in this repository are under the terms of the Apache License 2.0 Sipeed Ltd. except the third-party libraries or have their own license.

Community

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (4.12.5)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for Neucrack from gravatar.com Neucrack

Credits

Author: Sipeed

License

Apache Software License (Apache 2.0)

Tags

Machine vision AI vision IOT AIOT Edge computing

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

maixpy-4.12.1-cp313-cp313-manylinux2014_x86_64.whl (4.7 MB view details)

Uploaded CPython 3.13

maixpy-4.12.1-cp312-cp312-manylinux2014_x86_64.whl (4.7 MB view details)

Uploaded CPython 3.12

maixpy-4.12.1-cp311-cp311-manylinux2014_x86_64.whl (4.7 MB view details)

Uploaded CPython 3.11

maixpy-4.12.1-cp310-cp310-manylinux2014_x86_64.whl (4.7 MB view details)

Uploaded CPython 3.10

maixpy-4.12.1-cp39-cp39-manylinux2014_x86_64.whl (4.7 MB view details)

Uploaded CPython 3.9

maixpy-4.12.1-cp38-cp38-manylinux2014_x86_64.whl (4.7 MB view details)

Uploaded CPython 3.8

MaixPy-4.12.1-py3-none-any.whl (17.4 MB view details)

Uploaded Python 3

File details

Details for the file maixpy-4.12.1-cp313-cp313-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for maixpy-4.12.1-cp313-cp313-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3388ef954ccca6707b9d4a0306f6807c289cb4de55e700d5983d010fd89e24ce
MD5 1de7424daf0e63c82e666ba5fe88543d
BLAKE2b-256 89dead963d0499fc36c4c0ff9aa3f186dadc04596bf0eec7ea0d0dcbe852a607

See more details on using hashes here.

File details

Details for the file maixpy-4.12.1-cp312-cp312-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for maixpy-4.12.1-cp312-cp312-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b44ed7b3d62032458d948b2bf60a2b357a6d52b1bb8c3c92f641649f778dd83f
MD5 6d652e94f7f9cb577c71bd3ef609cfce
BLAKE2b-256 f79b034e5bd840b2a16b919602e8fc6d2247e680fbe7cfb899910705c40495a2

See more details on using hashes here.

File details

Details for the file maixpy-4.12.1-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for maixpy-4.12.1-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 fa5bd7f5472797791c5566364fb348db5758061382c679256505d0661b9c8627
MD5 f0c88d438c52b96c770d1b74b1ac572e
BLAKE2b-256 abc2f6f3adafdf85ca404cd0604fe88dd046d180c71adb86d41a3ef192167203

See more details on using hashes here.

File details

Details for the file maixpy-4.12.1-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for maixpy-4.12.1-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 89fca82f8ef8af54ea0d3d6d57aa82057b8cb58a2e14c65ed626e1add4ce1ba0
MD5 89d705863c4eac3e992a4739a365debe
BLAKE2b-256 00a15ab181d3424988c06688f9739d0ad85801d37db47160cbd3abc1c0bc3217

See more details on using hashes here.

File details

Details for the file maixpy-4.12.1-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for maixpy-4.12.1-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3eef9c15ce7dcd5420fe88ea97001e631baa6e4b39916d77b57261637ba5f652
MD5 8d78f8b539a5ff3427b924d89dc151f7
BLAKE2b-256 8d40a2ef36397f86511895e16f12408057efca30bd7725869e21043602bc8d6a

See more details on using hashes here.

File details

Details for the file maixpy-4.12.1-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for maixpy-4.12.1-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ddf40eee258bfd242c0917ba29fcf0824194cb24c76b43b0b2ffdfb4aea1186d
MD5 6f5ded1b85d351713e6b62f938b986f7
BLAKE2b-256 961984bfe1c3a084960ce8264210a35bed34a0a2214f4e62fd3f9933eb3e99c0

See more details on using hashes here.

File details

Details for the file MaixPy-4.12.1-py3-none-any.whl.

File metadata

  • Download URL: MaixPy-4.12.1-py3-none-any.whl
  • Upload date:
  • Size: 17.4 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.13

File hashes

Hashes for MaixPy-4.12.1-py3-none-any.whl
Algorithm Hash digest
SHA256 e9540667e8195698c75b5abc8fb1c0d32e24c6d01d189850fbf9e35726c13ba8
MD5 234dcdf1c5477b3f4153442aa7c9e1fa
BLAKE2b-256 62135d706d2204efcd1128ca16026ce7f4c8a0458130b6f3a293bbb3dbe3a2a7

See more details on using hashes here.

Release history Release notifications | RSS feed

4.12.5

5 files

4.12.4

5 files

This release

4.12.1 This release

7 files

4.12.0

7 files

4.11.9

7 files

4.11.8

7 files

4.11.7

7 files

4.11.6

7 files

4.11.5

6 files

4.11.4

5 files

4.11.3

6 files

4.11.2

5 files

4.11.1

5 files

4.11.0

5 files

4.10.3

6 files

4.10.2

6 files

4.10.1

6 files

4.10.0

6 files

4.9.3

6 files

4.9.2

6 files

4.9.1

6 files

4.9.0

4 files

4.8.2

6 files

4.8.1

4 files

4.8.0

6 files

4.7.8

6 files

4.7.7

6 files

4.7.6

6 files

4.7.5

6 files

4.7.3

6 files

4.7.2

6 files

4.7.1

4 files

4.7.0

4 files

4.6.0

6 files

4.5.1

6 files

4.5.0

4 files

4.4.22

6 files

4.4.21

4 files

4.4.20

6 files

4.4.19

6 files

4.4.18

6 files

4.4.17

6 files

4.4.16

6 files

4.4.15

6 files

4.4.13

6 files

4.4.12

6 files

4.4.11

4 files

4.4.10

6 files

4.4.9

4 files

4.4.8

4 files

4.4.7

4 files

4.4.6

4 files

4.4.5

4 files

4.4.2

4 files

4.4.0

4 files

4.3.7

6 files

4.3.6

4 files

4.3.5

4 files

4.3.4

6 files

4.3.3

2 files

4.3.2

2 files

4.2.1

1 file

4.1.4

1 file

4.1.3

1 file

4.1.2

1 file

4.1.0

1 file

4.0.11

1 file

4.0.10

1 file

4.0.7

1 file

4.0.6

1 file

4.0.5

2 files

4.0.4

1 file

4.0.3

2 files

4.0.2

1 file

4.0.1

1 file

4.0.0

3 files

0.0.1

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page