TOP 20 AI Open Source Projects in 2023

Artificial Intelligence (AI) is rapidly becoming a transformative force in the modern world, and open-source projects have played a significant role in this transformation. Open-source AI projects have democratized access to cutting-edge technology, encouraged collaboration among experts in the field, and enabled the development of sophisticated and robust AI solutions to address real-world problems.

In this article, we will highlight the top 20 AI open-source projects that are shaping the future of the field. These projects span a wide range of applications, from computer vision to natural language processing, and demonstrate the power of open-source software in advancing AI research and development. Whether you are a researcher, developer, or just curious about the latest trends in AI, these projects are sure to inspire and inform.

Contributing to AI open source projects is essential for several reasons:

1. It allows for the creation of high-quality tools and platforms that can be used by developers and researchers across the world, regardless of their location or financial resources. These projects democratize access to cutting-edge technology, making it possible for more people to participate in AI research, development, and innovation.
2. Open source projects encourage collaboration and knowledge-sharing among experts in the field. This collaboration results in the development of more sophisticated and robust AI solutions that can address complex real-world problems, from healthcare to climate change and beyond.
3. Contributing to open source projects is a great way to improve one's skills and knowledge in AI. By working on real-world projects with other experts in the field, individuals can gain valuable experience and knowledge that can be used in their careers.
4. Open source projects are instrumental in advancing the ethics and transparency of AI development. Through collaboration and peer review, experts can ensure that AI systems are developed in a responsible and ethical manner, without hidden biases or unintended consequences.
5. Contributing to AI open source projects is crucial for the democratization of AI technology, the advancement of the field, the improvement of one's skills, and the development of ethical and responsible AI solutions.

Here is the TOP 20 AI Open Source Projects:

1. TensorFlow

172,400 GitHub stars

TensorFlow is an Open Source Machine Learning Framework.

TensorFlow is a versatile, end-to-end open source platform that facilitates machine learning. It offers a comprehensive and flexible ecosystem of tools, libraries, and community resources, enabling researchers to push the boundaries of ML, and developers to easily build and deploy ML-powered applications.

Initially, TensorFlow was developed by the Google Brain team's researchers and engineers, working within Google's Machine Intelligence Research organization, to advance machine learning and deep neural network research. However, the system's flexibility and adaptability make it relevant in various other domains as well.

TensorFlow boasts stable Python and C++ APIs, along with a non-guaranteed backward compatible API, which supports other programming languages.

Learn More about TensorFlow: https://github.com/tensorflow/tensorflow

2. Hugging Face Transformers

(84,400 GitHub stars)

Transformers offers a plethora of pretrained models that can effectively tackle various tasks across different modalities such as text, vision, and audio. These models can be deployed to perform text-related tasks like text classification, information extraction, question answering, summarization, translation, and text generation, in more than 100 languages. They can also handle image-related tasks such as image classification, object detection, and segmentation, as well as audio-related tasks like speech recognition and audio classification. Additionally, transformer models can perform multitasking on various modalities, including table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.

Transformers provides a user-friendly interface to promptly access and leverage pretrained models on text, fine-tune them using customized datasets, and share them within the community through our model hub. Furthermore, each python module that defines an architecture is entirely self-contained and easily modifiable to facilitate quick research experiments.

Transformers is supported by the three most widely used deep learning libraries, namely Jax, PyTorch, and TensorFlow, with seamless integration among them. This integration enables easy training of models using one library, followed by loading them for inference with another library.

Learn More about Hugging Face Transformers: https://github.com/huggingface/transformers

3. OpenCV

 67,100 GitHub Stars

OpenCV, or the Open Source Computer Vision Library, is a potent tool for computer vision applications, including video, CCTV, and picture analysis. This library is published under a BSD license, making it freely available for both academic and commercial use.

The OpenCV library is based on C++ and boasts over 2,500 state-of-the-art and classic algorithms. These algorithms are capable of detecting faces in images or movies, identifying objects, and characterizing human emotions and behavior in videos. This AI open-source library can also be used to examine films and photos in all of their components, including the motion trail of objects, the extraction of three-dimensional models from these objects, and a variety of other applications.

The OpenCV library includes over 500 functions that cover a wide range of visual themes, such as industrial product inspection, medical imaging, security, user interface, camera calibration, stereo vision, and robotics. As computer vision and machine learning are often intertwined, OpenCV also includes a comprehensive Machine Learning Library (MLL). This sub-library is primarily concerned with statistical pattern detection and clustering. Although this machine learning library is particularly effective for computer vision problems, it can also be used for any machine learning problem.

Learn More about OpenCV: https://github.com/opencv/opencv

4. PyTorch

63,600 GitHub Stars

PyTorch is a Python package that offers two primary high-level features: tensor computation akin to NumPy, but with robust GPU acceleration, and deep neural networks that are built on a tape-based autograd system. The package can seamlessly incorporate a user's preferred Python packages, such as NumPy, SciPy, and Cython, to extend PyTorch as needed.

PyTorch is a library that comprises various components, including:

• torch, a tensor library that is similar to NumPy, but with strong GPU support. The library also includes torch.autograd, a tape-based automatic differentiation library that supports all differentiable tensor operations in torch
• torch.jit, a compilation stack (TorchScript) that generates serializable and optimizable models from PyTorch code.
• torch.nn, a neural networks library that is deeply integrated with autograd and designed to offer maximum flexibility.
• torch.multiprocessing, which provides Python multiprocessing capabilities, but with magical memory sharing of torch tensors across processes, making it particularly useful for data loading and Hogwild training.
• torch.utils, which includes DataLoader and other utility functions for user convenience.

PyTorch is typically used in one of two ways

• as a replacement for NumPy to leverage the power of GPUs
• as a deep learning research platform that provides maximum flexibility and speed.

Learn More about PyTorch: https://github.com/pytorch/pytorch

5. Keras

57,500 GitHub Stars

Keras is a high-level deep learning API that is implemented in Python, built on top of TensorFlow, the popular machine learning platform. Keras has been designed to facilitate fast experimentation while providing an exceptional developer experience.

Keras aims to provide developers with an edge when it comes to building machine learning-powered applications. It achieves this through the following characteristics:

• Simplicity: Keras aims to reduce the cognitive load on developers and allow them to concentrate on the essential aspects of the problem at hand. Keras emphasizes ease of use, conciseness, code elegance, debugging speed, maintainability, and deployability (via TFServing, TFLite, TF.js).
• Flexibility: Keras follows the principle of progressive disclosure of complexity, where simple workflows are easy and quick, while more advanced workflows are possible through a clear path that builds on the concepts learned earlier.
• Power: Keras delivers industry-level performance and scalability, as demonstrated by its adoption by leading organizations such as NASA, YouTube, and Waymo. Keras powers various functions, including the recommendation system for YouTube and the world's most advanced self-driving vehicle.

Keras provides a powerful and flexible deep learning platform with a focus on simplifying the developer experience, allowing for fast experimentation, and delivering exceptional performance and scalability.

Learn More about Keras: https://github.com/keras-team/keras

6. Stable Diffusion

45,100 GitHub Stars

Stable Diffusion is a cutting-edge latent text-to-image diffusion model that leverages advanced computational resources. This model was trained on a subset of the LAION-5B database using a Latent Diffusion Model and 512x512 images, thanks to the generous compute donation from Stability AI and support from LAION. Similar to Google's Imagen, the model utilizes a frozen CLIP ViT-L/14 text encoder to condition itself on text prompts. Despite its significant capabilities, the model is relatively lightweight, with an 860M UNet and a 123M text encoder, and requires a GPU with a minimum of 10GB VRAM. Please refer to the section below and the model card for further details.

Stable Diffusion v1 is a specific model configuration that employs an 860M UNet and a CLIP ViT-L/14 text encoder for the diffusion model, with a downsampling-factor 8 autoencoder. The model was pre-trained on 256x256 images and subsequently fine-tuned on 512x512 images.

Learn More about Stable Diffusion: https://github.com/CompVis/stable-diffusion

7. DeepFaceLab

37,800 GitHub Stars

DeepFaceLab is the leading open source software for creating deepfakes.

Deepfakes, which are created, modified, or synthesized utilizing deep learning, refer to manipulated images and videos. One prominent example is the replacement of a celebrity or politician's face in an existing image or video, often for humorous purposes but at times for malicious intentions. Python-based, open source DeepFaceLab represents a powerful deepfake technology capable of replacing faces in media, as well as removing wrinkles and other markers of aging.

Learn More about DeepFaceLab: https://github.com/iperov/DeepFaceLab

8. Detectron2

23,800 GitHub Stars

Detectron2 is a state-of-the-art library from Facebook AI Research that provides cutting-edge detection and segmentation algorithms for computer vision projects. It builds upon the success of its predecessor, Detectron, as well as maskrcnn-benchmark, and has been designed to support research and production applications within Facebook. The earlier version of Detectron was powered by Caffe, which made it difficult to use due to subsequent code modifications combining Caffe2 and PyTorch into a single repository. In response to feedback from the open-source community, Facebook AI released Detectron2 as an updated, user-friendly version of the original software system.

Detectron2 offers a range of advanced object identification algorithms, including DensePose, panoptic feature pyramid networks, and various variations of FAIR's pioneering Mask R-CNN model family. It supports object detection using boxes and instance segmentation masks, as well as human pose prediction, similar to Detectron. Moreover, Detectron2 includes support for semantic segmentation and panoptic segmentation, which combine semantic and instance segmentation for more precise detection and segmentation of objects in images and videos.

Learn More about Detectron2: https://github.com/facebookresearch/detectron2

9. Apache MXNet

20,300 GitHub Stars

Apache MXNet is a highly efficient and flexible deep learning framework that enables developers to blend symbolic and imperative programming for maximum productivity. At its core, MXNet features a dynamic dependency scheduler that parallelizes both symbolic and imperative operations in real time, and a graph optimization layer that enhances symbolic execution speed and memory efficiency. As a portable and lightweight tool, MXNet is scalable across multiple GPUs and machines.

Beyond its technical capabilities, MXNet represents a community that aspires to democratize AI. It offers guidelines and blueprints for building deep learning systems, and shares interesting insights on DL systems with hackers.

MXNet's features include a NumPy-like programming interface that integrates with the new and easy-to-use Gluon 2.0 interface, making it accessible to NumPy users looking to delve into deep learning. Additionally, automatic hybridization provides imperative programming with the speed of traditional symbolic programming. The framework is lightweight, memory-efficient, and portable to smart devices through native cross-compilation support on ARM, as well as ecosystem projects such as TVM, TensorRT, and OpenVINO.

MXNet also scales up to multi GPUs and distributed settings with auto parallelism through ps-lite, Horovod, and BytePS. Its extensible backend allows for full customization and integration with custom accelerator libraries and in-house hardware without the need for maintenance forks. Furthermore, MXNet supports a variety of programming languages, including Python, Java, C++, R, Scala, Clojure, Go, Javascript, Perl, and Julia. It is also cloud-friendly and compatible with AWS and Azure.

Learn More about MXNet: https://github.com/apache/mxnet

10. Fastai

23,500 GitHub Stars

Fastai is a comprehensive deep learning library designed to support practitioners in quickly and easily achieving state-of-the-art results in standard deep learning domains, as well as providing researchers with the flexibility to develop new approaches. It accomplishes this with a layered architecture that employs decoupled abstractions to express common underlying patterns of deep learning and data processing techniques. These abstractions are expressed concisely and clearly through the dynamism of Python language and the flexibility of the PyTorch library.

Fastai features a new type dispatch system for Python, as well as a semantic type hierarchy for tensors. Its GPU-optimized computer vision library is extendable in pure Python. Additionally, it provides an optimizer that refactors modern optimizers' common functionality into two basic pieces, allowing optimization algorithms to be implemented in a few lines of code. A novel 2-way callback system that can access any part of the data, model, or optimizer and change it at any point during training is also included. A new data block API, among many other features, further distinguishes fastai.

Fastai's architecture is built around two main design goals: to be approachable and productive, while also being hackable and configurable. This is achieved by leveraging a hierarchy of lower-level APIs that provide composable building blocks. Consequently, users who want to modify the high-level API or add specific behaviors to suit their needs do not need to learn the lowest level API.

Learn More about Fastai: https://github.com/fastai/fastai

11. Open Assistant

18,300 GitHub Stars

Open Assistant is a visionary project designed to democratize access to an exceptional chat-based large language model. As an open-source alternative to ChatGPT, it aims to spur a revolution in language innovation. Our vision is to catalyze groundbreaking advancements in language in the same way that Stable Diffusion has transformed the creation of art and images. Through the creation of Open Assistant, we hope to facilitate the advancement of language capabilities, ultimately contributing to the betterment of society as a whole.

Learn More about Open Assistant: https://github.com/LAION-AI/Open-Assistant

12. MindsDB

14,100 GitHub Stars

MindsDB is a promising open-source platform, aimed at empowering developers to construct AI-powered applications. It provides an automated and integrated approach to top machine learning frameworks, seamlessly incorporating them into data stacks. MindsDB offers an intuitive interface to train and deploy models as AI Tables in databases, simplifying the machine learning process and making it more accessible to developers of varying skill levels.

MindsDB offers versatile use cases for developers to build AI models, including Fraud Detection for detecting fraudulent activity in financial transactions and eCommerce platforms. Sales Forecasting, utilizing historical sales data to predict future sales and enable informed decision-making. Customer Segmentation for segmenting customers based on their behavior, preferences, and other factors, enabling personalized marketing. Sentiment analysis, harnessing pre-trained models such as GPT-3 and HuggingFace models to analyze text, such as customer reviews. Lastly, Predictive Maintenance, facilitating the development of models to predict equipment or machinery failures, enabling proactive maintenance and minimizing downtime.

Learn More about: https://github.com/mindsdb/mindsdb

13. DALL·E Mini

13,800 GitHub Stars

DALL-E mini, an online text-to-image generator, has recently experienced a surge in popularity on social media platforms. The application functions by converting a textual phrase, such as "mountain sunset," "Eiffel tower on the moon," or "Obama building a sandcastle," into an image representation.

Initially created as a submission for a coding competition by Texas-based computer engineer Boris Dayma, the app derives its name from its foundation on OpenAI's powerful DALL-E artificial intelligence technology. However, DALL-E mini, now renamed Craiyon at the request of the parent company, utilizes similar techniques in a more easily accessible web app format.

While access to OpenAI's models is restricted to most users, Dayma's model is readily available for use by anyone on the internet. The development of the model was accomplished through collaboration with AI research communities on Twitter and GitHub.

Learn More about DALL·E Mini: https://github.com/borisdayma/dalle-mini

14. Theano

9,700 GitHub Stars

Theano is an open-source artificial intelligence project developed by the MILA group at the University of Montreal in Montreal, Quebec, Canada. It is a Python library that facilitates mathematical operations on multi-dimensional arrays using NumPy or SciPy. Theano is capable of leveraging GPUs to accelerate processing and can generate symbolic graphs automatically for gradient computations.

Initially designed for implementing cutting-edge deep learning algorithms, Theano has become a standard in the industry for deep learning research and development. While it boasts exceptional computational performance, its users have expressed concerns about an unintuitive user interface and unhelpful error messages. As a result, Theano is typically employed in conjunction with more user-friendly wrappers like Keras, Lasagne, and Blocks, which are high-level frameworks for swift prototyping and model testing. Nevertheless, many data scientists still find Theano appealing due to its simplicity and maturity.

Theano streamlines the definition, optimization, and evaluation of various mathematical procedures. Additionally, Theano automatically computes gradients at multiple points, enabling gradient descent for model training.

Learn More about Theano: https://github.com/Theano/Theano

15. TFLearn

9,600 GitHub Stars

TFlearn is a deep learning library that is built on top of Tensorflow and aims to provide a higher-level API to facilitate and accelerate deep learning experiments while maintaining complete transparency and compatibility with Tensorflow.

TFlearn offers a user-friendly and easy-to-understand high-level API for developing deep neural networks, along with a broad range of built-in modular neural network layers, regularizers, optimizers, and metrics for rapid prototyping. Additionally, TFlearn provides full transparency over Tensorflow, allowing users to leverage the benefits of both libraries independently.

TFlearn also features a powerful set of helper functions to train any Tensorflow graph, and it enables easy and comprehensive visualization of deep learning models with detailed information about weights, gradients, activations, and more. Furthermore, TFlearn supports effortless device placement for using multiple CPUs/GPUs.

The high-level API of TFlearn supports various deep learning models, including Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, and Generative networks, among others. TFlearn aims to remain up-to-date with the latest deep learning techniques in the future. Please note that the latest version of TFlearn (v0.5) is compatible only with Tensorflow v2.0 and above.

Learn More about TFLearn: https://github.com/tflearn/tflearn

16. Ivy

9,400 GitHub Stars

Ivy is a state-of-the-art ML framework that currently supports JAX, TensorFlow, PyTorch, and Numpy. We are thrilled to invite you to experience its exceptional capabilities.

Our roadmap includes the integration of automatic code conversions between all frameworks, as well as instant multi-framework support for all open-source libraries with minimal code modifications. Please refer to the following sub-pages to learn more about Ivy's purpose, usage, upcoming features, and opportunities to contribute.

To indicate the current status of features, we use it to represent ongoing development and to denote implementation completion. Additionally, we offer Google Colabs with interactive demos for further exploration.

Please be aware that Ivy is still in the early stages of development. Therefore, anticipate the possibility of breaking changes and limitations until the upcoming release of version 1.2.0 within the next few weeks.

We welcome contributions from interested parties. Kindly consult our contributor guide and peruse the open tasks for further information on how to get started.

Learn More about Ivy: https://github.com/unifyai/ivy

17. YOLO7

9,200 GitHub Stars

YOLOv7 is the fastest and most accurate real-time object detection model for computer vision tasks.

Real-time object detection, a crucial area for various applications such as self-driving cars, robotics, and assistive devices, presents a complex challenge for artificial intelligence. Accurate identification of objects in images is imperative to ensure that these technologies collect and convey precise environmental information.

Among the open source object detection tools, YOLOv7 stands out as one of the most efficient and precise options available. By providing a set of images containing objects, this tool swiftly and accurately identifies them, delivering valuable insights into the observed scenes.

Learn More about YOLO7: https://github.com/WongKinYiu/yolov7

18. FauxPilot

8,000 GitHub Stars

FauxPilot offers coding assistance to programmers seeking support by training on existing production code and leveraging its knowledge to provide structured feedback and suggestions. The project was inspired by GitHub Copilot, but distinguishes itself by allowing users to select specific repositories for training.

This added level of control enables individuals to avoid utilizing code snippets from sources that do not grant approval, ensuring that the assistance and snippets obtained are reliable and free of potential legal concerns. By carefully selecting training sources and limiting them to those with proper permissions and licenses, programmers can increase the likelihood of receiving trustworthy and credible coding support.

Learn More about FauxPilot: https://github.com/fauxpilot/fauxpilot

19. PaddleNLP

7,900 GitHub Stars

PaddleNLP, a notable NLP library that exploits the PaddlePaddle framework, provides an easily accessible and robust toolset for a wide array of NLP tasks. Its impressive pre-trained model zoo supports research and industrial applications, enabling users to accomplish tasks such as Text Classification, Neural Search, Question Answering, Information Extraction, Document Intelligence, Sentiment Analysis, and Diffusion AIGC system, amongst others.

As natural language processing (NLP) engines become increasingly sophisticated, they can perform neural searches, sentiment analysis, and extract vital information for both human and machine users. Despite occasional hitches, this technology is highly versatile and applicable to diverse domains and use cases, such as in the case of Amazon's Alexa. With PaddleNLP, an open source NLP library, users can seamlessly gather search sentiments and identify essential entities.

Learn More about PaddleNLP: https://github.com/PaddlePaddle/PaddleNLP

20. DeepPavlov

6,100 GitHub Stars

DeepPavlov is a sophisticated open-source conversational AI library that is built using the PyTorch framework. Its primary aim is to facilitate the development of advanced chat-bots and intricate conversational systems. Additionally, the library enables research activities in the domain of natural language processing (NLP), with a specific focus on dialogue systems.

Numerous enterprises and large organizations are adopting chatbots as a replacement for customer service frontlines. As a consequence, machines are becoming increasingly capable of engaging in conversations. DeepPavlov integrates fundamental machine learning tools, such as TensorFlow, Keras, and PyTorch, to deliver chatbots that are capable of enhancing the user's learning experience. The outcomes achieved are often unconventional, unusual, and occasionally, with proper training, can prove to be valuable.

Learn More about DeepPavlov: https://github.com/deeppavlov/DeepPavlov