Skip to main content

Python version: 3.8 | 3.9 | 3.10 License PyPI version

This is the official repo of micromind, a toolkit that aims at bridging two communities: artificial intelligence and embedded systems. micromind is based on PyTorch and provides exportability for the supported models in ONNX, Intel OpenVINO, and TFLite.


💡 Key features

  • Smooth flow from research to deployment;
  • Support for multimedia analytics recipes (image classification, sound event detection, etc);
  • Detailed API documentation;
  • Tutorials for embedded deployment;

🛠️️ Installation

Using Pip

First of all, install Python 3.8 or later. Open a terminal and run:

pip install micromind

for the basic install. To install micromind with the full exportability features, run

pip install micromind[conversion]

From source

First of all, install Python 3.9 or later. Clone or download and extract the repository, navigate to <path-to-repository>, open a terminal and run:

pip install -e .

for the basic install. To install micromind with the full exportability features, run

pip install -e .[conversion]

Training networks with recipes

After the installation, get started looking at the examples and the docs!

Export your model and run it on your MCU

Check out this tutorial and have fun deploying your network on MCU!


📧 Contact

francescopaissan@gmail.com


Metadata

Release files for micromind 0.2.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for micromind 0.2.1
File Size Uploaded Provenance Actions
micromind-0.2.1.tar.gz 36.1 kB Without provenance Details for micromind-0.2.1.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for micromind 0.2.1
File Interpreter ABI Platform Actions
micromind-0.2.1-py3-none-any.whl Python 3 none any Details for micromind-0.2.1-py3-none-any.whl

Total release size: 76.1 kB

Release files / micromind-0.2.1.tar.gz

Download URL micromind-0.2.1.tar.gz
Size 36.1 kB
Tags Source
SHA-256 checksum
How to use checksums
118cfadf1afc5ad332982732a8b8dc85b2b01044848b23a24ad2e3dc83cfdb8e
BLAKE2b-256 checksum
How to use checksums
3cb64b514e7f12c6621a9edb32627a169dfa7e802bc2cbdae69e7390da8d128a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.11.7

Release files / micromind-0.2.1-py3-none-any.whl

Download URL micromind-0.2.1-py3-none-any.whl
Size 39.9 kB
Tags Python 3
SHA-256 checksum
How to use checksums
4e3bacbf212e79c9df05ce2ed91781dda58e357075745b81b44fa1dab02dd6d6
BLAKE2b-256 checksum
How to use checksums
8a3ede8e882b44734e6fab0e209e04aff034afc8e78891b59a0774e3e38c3537
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.11.7

Security for micromind 0.2.1

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

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