turicreate README

The project's README from github.com/apple/turicreate. As one markdown file: README.md.

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Turi Create

Turi Create

Turi Create simplifies the development of custom machine learning models. You don't have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app.

With Turi Create, you can accomplish many common ML tasks:

ML Task Description
Recommender Personalize choices for users
Image Classification Label images
Drawing Classification Recognize Pencil/Touch Drawings and Gestures
Sound Classification Classify sounds
Object Detection Recognize objects within images
One Shot Object Detection Recognize 2D objects within images using a single example
Style Transfer Stylize images
Activity Classification Detect an activity using sensors
Image Similarity Find similar images
Classifiers Predict a label
Regression Predict numeric values
Clustering Group similar datapoints together
Text Classifier Analyze sentiment of messages

Example: Image classifier with a few lines of code

If you want your app to recognize specific objects in images, you can build your own model with just a few lines of code:

import turicreate as tc

# Load data 
data = tc.SFrame('photoLabel.sframe')

# Create a model
model = tc.image_classifier.create(data, target='photoLabel')

# Make predictions
predictions = model.predict(data)

# Export to Core ML
model.export_coreml('MyClassifier.mlmodel')

It's easy to use the resulting model in an iOS application:

Turi Create

Supported Platforms

Turi Create supports:

System Requirements

Turi Create requires:

Installation

For detailed instructions for different varieties of Linux see LINUX_INSTALL.md. For common installation issues see INSTALL_ISSUES.md.

We recommend using virtualenv to use, install, or build Turi Create. 

pip install virtualenv

The method for installing Turi Create follows the standard python package installation steps. To create and activate a Python virtual environment called venv follow these steps:

# Create a Python virtual environment
cd ~
virtualenv venv

# Activate your virtual environment
source ~/venv/bin/activate

Alternatively, if you are using Anaconda, you may use its virtual environment:

conda create -n virtual_environment_name anaconda
conda activate virtual_environment_name

To install Turi Create within your virtual environment:

(venv) pip install -U turicreate

Documentation

The package User Guide and API Docs contain more details on how to use Turi Create.

GPU Support

Turi Create does not require a GPU, but certain models can be accelerated 9-13x by utilizing a GPU.

Linux macOS 10.13+ macOS 10.14+ discrete GPUs, macOS 10.15+ integrated GPUs
Activity Classification Image Classification Activity Classification
Drawing Classification Image Similarity Object Detection
Image Classification Sound Classification One Shot Object Detection
Image Similarity Style Transfer
Object Detection
One Shot Object Detection
Sound Classification
Style Transfer

macOS GPU support is automatic. For Linux GPU support, see LinuxGPU.md.

Building From Source

If you want to build Turi Create from source, see BUILD.md.

Contributing

Prior to contributing, please review CONTRIBUTING.md and do not provide any contributions unless you agree with the terms and conditions set forth in CONTRIBUTING.md.

We want the Turi Create community to be as welcoming and inclusive as possible, and have adopted a Code of Conduct that we expect all community members, including contributors, to read and observe.

turicreate (apple/turicreate)

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这是什么？Turi Create 是 Apple 推出的一个 Python 库，让你无需成为机器学习专家，也能为推荐、目标检测、图像分类、图像相似度和活动分类等任务构建自定义机器学习模型。
它是 Python 库还是独立应用？它是一个 Python 包（底层为 C++ 核心），你可以将其导入自己的脚本中使用。只需几行代码即可训练模型，然后将其导出。
我该如何安装并开始使用？创建 Python 虚拟环境（venv 或 Anaconda），激活该环境，然后在其中以标准 pip 包的方式安装 Turi Create。
我需要什么 Python 版本和硬件？它需要 Python 2.7 或 3.5–3.8，并运行于 x86_64 架构上，至少需要 4 GB 内存；GPU 为可选项，但能将部分模型的速度提升 9–13 倍。
这个项目还在维护吗？否——该仓库已归档，不再接收更新，也不再提供积极支持，尽管它曾经的功能范围非常广泛。
我可以在哪些操作系统上运行它？它可在 macOS 10.12+、Linux（glibc 2.10+）上运行，也可在 Windows 10 上通过 WSL 运行。
我可以在我的 iOS 应用中使用训练好的模型吗？可以——模型可直接导出为 Core ML 格式，用于 iOS、macOS、watchOS 和 tvOS 应用。
它能处理哪些类型的数据？Turi Create 非常灵活，训练时支持文本、图像、音频、视频和传感器数据。
它能扩展到大型数据集吗？它的设计目标是在单台机器上高效处理大型数据集，无需集群也能做到快速且可扩展。
它开箱即用支持哪些机器学习任务？它内置了推荐系统、图像/绘图/声音/活动分类、目标检测（包括单样本检测）、风格迁移、图像相似度、通用分类器、回归、聚类和文本分类。
What are similar projects?albumentations, AFNetworking, opencv_contrib, firebase-ios-sdk, Detectron, X-AnyLabeling, opencv, fiftyone
Which topics is it in?recommendation-system, watchos, tvos, image-classification, object-detection, apple, image-processing, bsd, deprecated, computer-vision, sdk, c++
Creator
apple
Size
313.2 MB
Stars
11.2K
Updated
CommitNov 29Release6.4.1Sep 30
Age
8y
Commits · 90 Days