As July draws to a close, MyBridge has selected 10 of the best machine learning open source projects for developers:

  • These projects have an average of 1,041 stars on GitHub
  • Topics covered: human posture prediction, image classification, mobile AI computing engine, medical detection, satellite imagery, etc.

No.1

DensePose: Map all human pixels in 2D RGB images in real time to a surface-based 3D model of human body. [GitHub 3000 stars]

Project address: Stamp here

No.2

Darts: Differentiable framework Search for Convolutional and Recurrent Neural Networks. [1187 stars on GitHub]

Stamp here

No.3

SNIPER: A very efficient multi-scale target detection algorithm. [1388 stars on GitHub]

Stamp here

No.4

Mace: Xiaomi’s open source deep learning framework for mobile computing platforms. [GitHub 2182 stars]

Project address: Stamp here

No.5

RoboSat: It can semantically segment aerial and satellite images to extract important features such as buildings, parking lots, roads and water. [GitHub 810 stars]

Project address: Stamp here

No.6

DecaNLP: A multi-tasking challenge in the field of natural language processing, covering 10 tasks: question answering, machine translation, text review, natural language reasoning, semantic analysis, semantic tagging, zero-sample relation extraction, goal-oriented dialogue, semantic parsing, and common sense noun parsing. [948 stars on GitHub]

Stamp here

No.7

Magnitude: A fast and easy vector embedding entity library. [434 stars on GitHub]

Project address: Stamp here

No.8

Porcupine: a device-based language-wake detection engine driven by deep learning that enables developers to build language-enabled, always-listening applications or platforms. [379 stars on GitHub]

Project address: Stamp here

No.9

NCRF: Detection of cancer metastasis using neural Conditional random field. [297 stars on GitHub]

Stamp here

No.10

DALI: Includes highly optimized building blocks and an execution engine for data preprocessing in deep learning applications. [GitHub 450 stars]

Project address: Stamp here


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