Skip to main content

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

New MaixPy (v4) and new hardware platform MaixCAM is coming now!
If you have any suggestions, tell us on MaixHub, or Telegram/MaixPy or QQ group: 862340358.

Click the Star in the upper right corner to let us know you like it to encourage us to add more features.

Introduction

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 video:

Classifier Result video

Simply use 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))

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

MaixVision

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

MaixHub

Features

Python programing, MaixVision Workstation, AI vision, video streaming, voice recognize, peripheral usage etc.

Details and videos visit official site: wiki.sipeed.com/maixpy/

Hardware platform MaixCAM

And we provide new powerful hardware platform MaixCAM and MaixCAM-Pro:

MaixCAM

CPU NPU Memory
- 1GHz RISC-V(Linux)/ARM A53
- 700MHz RISCV-V(RTOS)
- 25~300MHz 8051(LowPower)
1Tops@INT8 NPU, support BF16
support YOLOv5 YOLOv8 etc.
256MB DDR3
Connecting Peripheral MultiMedia Buy
USB2.0/WiFi6/BLE5.4 IIC/PWM/SPI/UART/WDT/ADC - 4M Camera
- 2.3" 552x368 Touchscreen
- H.264/H.265/MJPEG codec
Sipeed Official Store

Chip register level open, more detalils: MaixCAM-Pro or MaixCAM

  • Maix-I K210 series is outdated, MaixPy v4 not support it, use it please visit MaixPy-v1

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.

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 Maix-II v831 MaixCAM
CPU 400MHz RISC-V x2 800MHz ARM7 1GHz RISC-V(Linux)
700MHz RISC-V(RTOS)
25~300MHz 8051(Low Power)
Memory 6MB SRAM 64MB DDR2 256MB DDR3
NPU 0.25Tops@INT8
official says 1T but...
0.25Tops@INT8 1Tops@INT8
Encoder 1080p@30fps 2K@30fps
Screen 2.4" 320x240 1.3" 240x240 2.28" 552x368 / 5" 1280x720 / 7" 1280x800 / 10“ 1280x800
TouchScreen 2.3" 552x368
Camera 30W 200W 500W
WiFi 2.4G 2.4G WiFi6 2.4G/5G
USB USB2.0 USB2.0
Eth 100M(Optional) 100M(Optional)
SD Interface SPI SDIO SDIO
BLE BLE5.4
OS RTOS Tina Linux Linux + RTOS
Language C / C++ / MicroPython C / C++ / Python3 C / C++ / Python3
Software MaixPy MaixPy3 MaixCDK + MaixPy v4 + opencv + numpy + ...
PC software MaixPy IDE MaixPy3 IDE MaixVision Workstation
Docs ⭐️⭐️⭐️⭐️ ⭐️⭐️⭐️ 🌟🌟🌟🌟🌟
Online AI train ⭐️⭐️⭐️ ⭐️⭐️⭐️⭐️ 🌟🌟🌟🌟🌟
Official APPs ⭐️⭐️ ⭐️⭐️⭐️ 🌟🌟🌟🌟🌟
AI classify(224x224) MobileNetv1 50fps
MobileNetv2 ✖
Resnet ✖
MobileNet ✖
Resnet18 20fps
Resnet50 ✖
MobileNetv2 130fps
Resnet18 62fps
Resnet50 28fps
AI detect(NPU forward part) YOLOv2(224x224) 15fps YOLOv2(224x224) 15fps YOLOv5s(224x224) 100fps
YOLOv5s(320x256) 70fps
YOLOv5s(640x640) 15fps
YOLOv8n(640x640) 23fps
YOLO11n(224x224)175fps
YOLO11n(320x224)120fps
YOLO11n(320x320)95fps
YOLO11n(640x640)23fps
Ease of use ⭐️⭐️⭐️⭐️ ⭐️⭐️⭐️ 🌟🌟🌟🌟🌟

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:

What difference between MaixPy v1, MaixPy3 and MaixPy v4?

  • MaixPy v1 use MicroPython programming language, only support Sipeed Maix-I K210 series hardware, have limited third-party packages.
  • MaixPy3 is designed for Sipeed Maix-II-Dock v831, not a long-term support version.
  • MaixPy v4 use Python programming language, so there's much package we can use directly. MaixPy v4 support new hardware platforms of Sipeed, it's a long-term support version, the future's hardware platforms will support this version. MaixPy v4 have a MaixPy-v1 compatible API, so you can quickly migrate your MaixPy v1 project to MaixPy v4.

(MaixPy v4 Will not support Maix-I K210 series, if you are using Maix-I K210 series, it's recommended to upgrade hardware platform to use this to get more features and better performance.)

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

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-4.9.2-py3-none-any.whl (14.2 MB view details)

Uploaded Python 3

MaixPy-4.9.2-cp312-cp312-manylinux2014_x86_64.whl (3.6 MB view details)

Uploaded CPython 3.12

MaixPy-4.9.2-cp311-cp311-manylinux2014_x86_64.whl (3.6 MB view details)

Uploaded CPython 3.11

MaixPy-4.9.2-cp310-cp310-manylinux2014_x86_64.whl (3.6 MB view details)

Uploaded CPython 3.10

MaixPy-4.9.2-cp39-cp39-manylinux2014_x86_64.whl (3.6 MB view details)

Uploaded CPython 3.9

MaixPy-4.9.2-cp38-cp38-manylinux2014_x86_64.whl (3.6 MB view details)

Uploaded CPython 3.8

File details

Details for the file MaixPy-4.9.2-py3-none-any.whl.

File metadata

  • Download URL: MaixPy-4.9.2-py3-none-any.whl
  • Upload date:
  • Size: 14.2 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.11.11

File hashes

Hashes for MaixPy-4.9.2-py3-none-any.whl
Algorithm Hash digest
SHA256 f066ac2f18455c083b230ea3f2f4cdc5cba35c1680465f13b17315c23ebdf4f0
MD5 fdc693aa6234aa950cfd7dc6445df7c1
BLAKE2b-256 7b74144dd7049f5dbbb477697b01a788257a0674f90f2514b55c0d0da63e344a

See more details on using hashes here.

File details

Details for the file MaixPy-4.9.2-cp312-cp312-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for MaixPy-4.9.2-cp312-cp312-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 27820d5e60cad1fc2b9095a9a0a4630bb51b91d807a3177fca47a2b2bc5641fc
MD5 f082e4842158a026d23a87142570c899
BLAKE2b-256 f691e8e23215e71b887394995c536215dc609585e7126dd3beb80c6bd6fc27d4

See more details on using hashes here.

File details

Details for the file MaixPy-4.9.2-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for MaixPy-4.9.2-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ccf9593d9e66a61aadeb425f19a7e62ae6b8509d830748fdd26bf244dd52a090
MD5 05e9ed520c0e752d8b6a0407f887e3e7
BLAKE2b-256 186bb94a4fc7fedd02132c6cfd892f03f58bb8db2760ba63518092d91f91f54e

See more details on using hashes here.

File details

Details for the file MaixPy-4.9.2-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for MaixPy-4.9.2-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b0d94c75d3e63e5303d8f4d32fe76b951f7c713bf90b0664aaee4b8df93e3545
MD5 1e0f22437620b351fb9bd8bd0da92a94
BLAKE2b-256 e1dc2da8dc4676a53d87f50a282e7a763c33213403f72ea663f8410596b448d0

See more details on using hashes here.

File details

Details for the file MaixPy-4.9.2-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for MaixPy-4.9.2-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 740d05b4cdbcdac56b0f1c169a4f39739cf64b92dd678c67dd779f56b01a9757
MD5 76cb5bf2270092cb0556c9266aa02a10
BLAKE2b-256 8b13b7c3025d7dea38066efe497155ffedb1d9a7545901c6d2858666742651b3

See more details on using hashes here.

File details

Details for the file MaixPy-4.9.2-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for MaixPy-4.9.2-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f877e86b3f1f0fd7fb6c88e42f461f26beab8cc90557dfd82d0c12e2ba225374
MD5 f97757ae3dfd6f837e385dbc41e0c93b
BLAKE2b-256 3564648da441c3fc67ff79fc127035b31cb84f90b22b54c0229191994b3ba5cf

See more details on using hashes here.

Release history Release notifications | RSS feed

4.12.5

5 files

4.12.4

5 files

4.12.1

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

This release

4.9.2 This release

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