Alternatives to Lightly logo

Alternatives to Lightly

TensorFlow, PyTorch, Keras, scikit-learn, and OpenCV are the most popular alternatives and competitors to Lightly.
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What is Lightly and what are its top alternatives?

Data labeling is time-consuming and can be very expensive. Lightly tells companies which data to label to have the biggest impact on model accuracy while saving time and costs.
Lightly is a tool in the Image Processing and Management category of a tech stack.
Lightly is an open source tool with GitHub stars and GitHub forks. Here’s a link to Lightly's open source repository on GitHub

Top Alternatives to Lightly

  • TensorFlow

    TensorFlow

    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. ...

  • PyTorch

    PyTorch

    PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc. ...

  • Keras

    Keras

    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/ ...

  • scikit-learn

    scikit-learn

    scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license. ...

  • OpenCV

    OpenCV

    OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform. ...

  • Cloudinary

    Cloudinary

    Cloudinary is a cloud-based service that streamlines websites and mobile applications' entire image and video management needs - uploads, storage, administration, manipulations, and delivery. ...

  • FFMPEG

    FFMPEG

    The universal multimedia toolkit.

  • imgix

    imgix

    imgix is a real-time image processing service and CDN. Resize, crop, and edit images simply by changing their URLs. ...

Lightly alternatives & related posts

TensorFlow logo

TensorFlow

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2.7K
76
Open Source Software Library for Machine Intelligence
2.5K
2.7K
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PROS OF TENSORFLOW
  • 24
    High Performance
  • 16
    Connect Research and Production
  • 13
    Deep Flexibility
  • 9
    Auto-Differentiation
  • 9
    True Portability
  • 2
    Easy to use
  • 2
    High level abstraction
  • 1
    Powerful
CONS OF TENSORFLOW
  • 8
    Hard
  • 5
    Hard to debug
  • 1
    Documentation not very helpful

related TensorFlow posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.3M views

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

See more

In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

!

See more
PyTorch logo

PyTorch

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A deep learning framework that puts Python first
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PROS OF PYTORCH
  • 13
    Easy to use
  • 11
    Developer Friendly
  • 10
    Easy to debug
  • 7
    Sometimes faster than TensorFlow
CONS OF PYTORCH
  • 3
    Lots of code

related PyTorch posts

Server side

We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

  • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

  • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

  • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

Client side

  • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

  • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

  • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

Cache

  • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

Database

  • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

Infrastructure

  • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

Other Tools

  • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

  • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

See more
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.3M views

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

See more
Keras logo

Keras

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Deep Learning library for Theano and TensorFlow
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PROS OF KERAS
  • 5
    Easy and fast NN prototyping
  • 5
    Quality Documentation
  • 4
    Supports Tensorflow and Theano backends
CONS OF KERAS
  • 3
    Hard to debug

related Keras posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.3M views

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

See more

I am going to send my website to a Venture Capitalist for inspection. If I succeed, I will get funding for my StartUp! This website is based on Django and Uses Keras and TensorFlow model to predict medical imaging. Should I use Heroku or PythonAnywhere to deploy my website ?? Best Regards, Adarsh.

See more
scikit-learn logo

scikit-learn

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Easy-to-use and general-purpose machine learning in Python
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+ 1
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PROS OF SCIKIT-LEARN
  • 20
    Scientific computing
  • 15
    Easy
CONS OF SCIKIT-LEARN
  • 1
    Limited

related scikit-learn posts

OpenCV logo

OpenCV

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Open Source Computer Vision Library
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PROS OF OPENCV
  • 29
    Computer Vision
  • 17
    Open Source
  • 11
    Imaging
  • 9
    Machine Learning
  • 8
    Face Detection
  • 6
    Great community
  • 4
    Realtime Image Processing
  • 2
    Image Augmentation
  • 1
    Helping almost CV problem
CONS OF OPENCV
    Be the first to leave a con

    related OpenCV posts

    Shared insights
    on
    FFMPEG
    OpenCV

    Hi Team,

    Could you please suggest which one need to be used in between OpenCV and FFMPEG.

    Thank you in Advance.

    See more
    Cloudinary logo

    Cloudinary

    458
    458
    176
    An end-to-end image & video management solution for your web and mobile applications
    458
    458
    + 1
    176
    PROS OF CLOUDINARY
    • 36
      Easy setup
    • 30
      Fast image delivery
    • 26
      Vast array of image manipulation capabilities
    • 20
      Free tier
    • 11
      Heroku add-on
    • 9
      Reduce development costs
    • 7
      Amazing support
    • 6
      Virtually limitless scale
    • 6
      Great libraries for all languages
    • 6
      Heroku plugin
    • 5
      Easy to integrate with Rails
    • 4
      Cheap
    • 3
      Shot setup time
    • 3
      Very easy setup
    • 2
      Solves alot of image problems.
    • 1
      Best in the market and includes free plan
    • 1
      Extremely generous free pricing tier
    • 0
      Fast image delivery, vast array
    CONS OF CLOUDINARY
    • 2
      Paid plan is expensive

    related Cloudinary posts

    FFMPEG logo

    FFMPEG

    206
    157
    4
    The universal multimedia toolkit.
    206
    157
    + 1
    4
    PROS OF FFMPEG
    • 4
      Open Source
    CONS OF FFMPEG
      Be the first to leave a con

      related FFMPEG posts

      Shared insights
      on
      FFMPEG
      OpenCV

      Hi Team,

      Could you please suggest which one need to be used in between OpenCV and FFMPEG.

      Thank you in Advance.

      See more
      imgix logo

      imgix

      186
      251
      140
      Real-time image resizing service and CDN
      186
      251
      + 1
      140
      PROS OF IMGIX
      • 25
        Image processing on demand
      • 22
        Easy setup
      • 16
        Reduce Development Costs
      • 15
        Smart Cropping
      • 13
        Efficient
      • 10
        Insanely Fast
      • 9
        Filters, resizing, blur and more as url parameters
      • 8
        Easy to understand pricing
      • 7
        Professional Features and Options
      • 4
        Lightyears better than ImageMagick
      • 4
        Excellent Face Detection
      • 3
        S3 as source
      • 2
        Great for Dynamic Compositing
      • 2
        Scales to your company's needs
      CONS OF IMGIX
        Be the first to leave a con

        related imgix posts

        Mountain/ \Ash

        Platform Update: we’ve been using the Performance Test tool provided by KeyCDN for a long time in combination with Pingdom's similar tool and the #WebpageTest and #GoogleInsight - we decided to test out KeyCDN for static asset hosting. The results for the endpoints were superfast - almost 200% faster than CloudFlare in some tests and 370% faster than imgix . So we’ve moved Washington Brown from imgix for hosting theme images, to KeyCDN for hosting all images and static assets (Font, CSS & JS). There’s a few things that we like about “Key” apart from saving $6 a month on the monthly minimum spend ($4 vs $10 for imgix). Key allow for a custom CNAME (no more advertising imgix.com in domain requests and possible SEO improvements - and easier to swap to another host down the track). Key allows JPEG/WebP image requests based on clients ‘accept’ http headers - imgix required a ?auto=format query string on each image resource request - which can break some caches. Key allows for explicitly denying cookies to be set on a zone/domain; cookies are a big strain on limited upload bandwidth so to be able to force these off is great - Cloudflare adds a cookie to every header… for “performance reasons”… but remember “if you’re getting a product something for free…”

        See more
        Mountain/ \Ash

        In mid-2018 we made a big push for speed on the site. The site, running on PHP, was taking about 7 seconds to load. The site had already been running through CloudFlare for some time but on a shared host in Sydney (which is also where most of the customers are). We found when developing the @TuffTruck site that DigitalOcean was fast - and even though it's located overseas, we still found it 2 seconds faster for Australian users. We found that some Wordpress plugins were really slowing the TTFB - with all plugins off, Wordpress would save respond 1.5-2 seconds faster. With a on/off step through of each plugin we found 2 plugins by Ontraport (a CRM type service that some forms we populating) was the main culprit. Out it went and we built our own WP plugin to do push the data to Ontraport only when required. With the TTFB acceptable, we moved on to getting the completed page load time down. Turning on CloudFlare 's HTML/CSS/JS minifications & Rocket Loader we could get our group of test pages, including the homepage, loading [in full] in just over 2 seconds. We then moved images off to imgix and put the CSS, JS and Fonts onto a mirrored subdomain (so that cookies weren't exchanged), but this only shaved about another 0.2 seconds off. We are keeping it running for the moment, but the $10 minimum a month for imgix is hardly worth it (this would be change if new images were going up all the time and needed processing). The client is overly happy with the ~70% improvement and has already seen the site move up the ranks of Google's SERP and bring down their PPC costs. AND all the new hosting providers still come in at half the price of the previous Sydney hosting service. We have a few ideas that we are testing on our staging site and will roll these out soon.

        See more