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Software by apple

coremltools
Open Source

coremltools

[![Build Status](https://img.shields.io/gitlab/pipeline/coremltools1/coremltools/main)](https://gitlab.com/coremltools1/coremltools/-/pipelines?page=1&scope=branches&ref=main) [![PyPI Release](https://img.shields.io/pypi/v/coremltools.svg)](#) [![Python Versions](https://img.shields.io/pypi/pyversions/coremltools.svg)](#) [Core ML Tools](https://apple.github.io/coremltools/docs-guides/source/overview-coremltools.html) ======================= ![Core ML Tools logo](docs/logo.png) Use [Core ML Tools](https://apple.github.io/coremltools/docs-guides/source/overview-coremltools.html) (*coremltools*) to convert machine learning models from third-party libraries to the Core ML format. This Python package contains the supporting tools for converting models from training libraries such as the following: * [TensorFlow 1.x](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf) * [TensorFlow 2.x](https://www.tensorflow.org/api_docs) * [PyTorch](https://pytorch.org/) * Non-neural network frameworks: * [scikit-learn](https://scikit-learn.org/stable/) * [XGBoost](https://xgboost.readthedocs.io/en/latest/) * [LibSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) With coremltools, you can: * Convert trained models to the Core ML format. * Read, write, and optimize Core ML models. * Verify conversion/creation (on macOS) by making predictions using Core ML. After conversion, you can integrate the Core ML models with your app using Xcode. ## Install The latest stable version is available from https://pypi.org/project/coremltools/ To install it use `pip` or the Python package manager of your choice. ```shell pip install coremltools ``` ## Core ML [Core ML](https://developer.apple.com/documentation/coreml) is an Apple framework to integrate machine learning models into your app. Core ML provides a unified representation for all models. Your app uses Core ML APIs and user data to make predictions, and to fine-tune models, all on the user’s device. Core ML optimizes on-device performance by leveraging the CPU, GPU, and Neural Engine while minimizing its memory footprint and power consumption. Running a model strictly on the user’s device removes any need for a network connection, which helps keep the user’s data private and your app responsive. ## Resources To install coremltools, see [Installing Core ML Tools](https://apple.github.io/coremltools/docs-guides/source/installing-coremltools.html). For more information, see the following: * [Release Notes](https://github.com/apple/coremltools/releases/) * [Guide and examples](https://apple.github.io/coremltools/docs-guides/index.html) * [API Reference](https://apple.github.io/coremltools/index.html) * [Core ML Specification](https://apple.github.io/coremltools/mlmodel/index.html) * [Building from Source](BUILDING.md) * [Contribution Guidelines](CONTRIBUTING.md)

ML Frameworks
5.3K Github Stars
ml-cvnets
Open Source

ml-cvnets

# CVNets: A library for training computer vision networks CVNets is a computer vision toolkit that allows researchers and engineers to train standard and novel mobile- and non-mobile computer vision models for variety of tasks, including object classification, object detection, semantic segmentation, and foundation models (e.g., CLIP). ## Table of contents * [What's new?](#whats-new) * [Installation](#installation) * [Getting started](#getting-started) * [Supported models and tasks](#supported-models-and-tasks) * [Maintainers](#maintainers) * [Research effort at Apple using CVNets](#research-effort-at-apple-using-cvnets) * [Contributing to CVNets](#contributing-to-cvnets) * [License](#license) * [Citation](#citation) ## What's new? * ***July 2023***: Version 0.4 of the CVNets library includes * [Bytes Are All You Need: Transformers Operating Directly On File Bytes ](https://arxiv.org/abs/2306.00238) * [RangeAugment: Efficient online augmentation with Range Learning](https://arxiv.org/abs/2212.10553) * Training and evaluating foundation models (CLIP) * Mask R-CNN * EfficientNet, Swin Transformer, and ViT * Enhanced distillation support ## Installation We recommend to use Python 3.10+ and [PyTorch](https://pytorch.org) (version >= v1.12.0) Instructions below use Conda, if you don't have Conda installed, you can check out [How to Install Conda](https://docs.conda.io/en/latest/miniconda.html#latest-miniconda-installer-links). ```bash # Clone the repo git clone [email protected]:apple/ml-cvnets.git cd ml-cvnets # Create a virtual env. We use Conda conda create -n cvnets python=3.10.8 conda activate cvnets # install requirements and CVNets package pip install -r requirements.txt -c constraints.txt pip install --editable . ``` ## Getting started * General instructions for working with CVNets are given [here](docs/source/en/general). * Examples for training and evaluating models are provided [here](docs/source/en/models) and [here](examples). * Examples for converting a PyTorch model to CoreML are provided [here](docs/source/en/general/README-pytorch-to-coreml.md). ## Supported models and Tasks To see a list of available models and benchmarks, please refer to [Model Zoo](docs/source/en/general/README-model-zoo.md) and [examples](examples) folder. <details> <summary> ImageNet classification models </summary> * CNNs * [MobileNetv1](https://arxiv.org/abs/1704.04861) * [MobileNetv2](https://arxiv.org/abs/1801.04381) * [MobileNetv3](https://arxiv.org/abs/1905.02244) * [EfficientNet](https://arxiv.org/abs/1905.11946) * [ResNet](https://arxiv.org/abs/1512.03385) * [RegNet](https://arxiv.org/abs/2003.13678) * Transformers * [Vision Transformer](https://arxiv.org/abs/2010.11929) * [MobileViTv1](https://arxiv.org/abs/2110.02178) * [MobileViTv2](https://arxiv.org/abs/2206.02680) * [SwinTransformer](https://arxiv.org/abs/2103.14030) </details> <details> <summary> Multimodal Classification </summary> * [ByteFormer](https://arxiv.org/abs/2306.00238) </details> <details> <summary> Object detection </summary> * [SSD](https://arxiv.org/abs/1512.02325) * [Mask R-CNN](https://arxiv.org/abs/1703.06870) </details> <details> <summary> Semantic segmentation </summary> * [DeepLabv3](https://arxiv.org/abs/1706.05587) * [PSPNet](https://arxiv.org/abs/1612.01105) </details> <details> <summary> Foundation models </summary> * [CLIP](https://arxiv.org/abs/2103.00020) </details> <details> <summary> Automatic Data Augmentation </summary> * [RangeAugment](https://arxiv.org/abs/2212.10553) * [AutoAugment](https://arxiv.org/abs/1805.09501) * [RandAugment](https://arxiv.org/abs/1909.13719) </details> <details> <summary> Distillation </summary> * Soft distillation * Hard distillation </details> ## Maintainers This code is developed by <a href="https://sacmehta.github.io" target="_blank">Sachin</a>, and is now maintained by Sachin, <a href="https://mchorton.com" target="_blank">Maxwell Horton</a>, <a href="https://www.mohammad.pro" target="_blank">Mohammad Sekhavat</a>, and Yanzi Jin. ### Previous Maintainers * <a href="https://farzadab.github.io" target="_blank">Farzad</a> ## Research effort at Apple using CVNets Below is the list of publications from Apple that uses CVNets: * [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer, ICLR'22](https://arxiv.org/abs/2110.02178) * [CVNets: High performance library for Computer Vision, ACM MM'22](https://arxiv.org/abs/2206.02002) * [Separable Self-attention for Mobile Vision Transformers (MobileViTv2)](https://arxiv.org/abs/2206.02680) * [RangeAugment: Efficient Online Augmentation with Range Learning](https://arxiv.org/abs/2212.10553) * [Bytes Are All You Need: Transformers Operating Directly on File Bytes](https://arxiv.org/abs/2306.00238) ## Contributing to CVNets We welcome PRs from the community! You can find information about contributing to CVNets in our [contributing](CONTRIBUTING.md) document. Please remember to follow our [Code of Conduct](CODE_OF_CONDUCT.md). ## License For license details, see [LICENSE](LICENSE). ## Citation If you find our work useful, please cite the following paper: ``` @inproceedings{mehta2022mobilevit, title={MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer}, author={Sachin Mehta and Mohammad Rastegari}, booktitle={International Conference on Learning Representations}, year={2022} } @inproceedings{mehta2022cvnets, author = {Mehta, Sachin and Abdolhosseini, Farzad and Rastegari, Mohammad}, title = {CVNets: High Performance Library for Computer Vision}, year = {2022}, booktitle = {Proceedings of the 30th ACM International Conference on Multimedia}, series = {MM '22} } ```

Developer Tools ML Frameworks
2K Github Stars