Software Alternatives, Accelerators & Startups

Labelbox VS neptune.ai

Compare Labelbox VS neptune.ai and see what are their differences

Labelbox logo Labelbox

Build computer vision products for the real world

neptune.ai logo neptune.ai

Neptune brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed and shared with others. Works with all common technologies and integrates with other tools.
  • Labelbox Landing page
    Landing page //
    2023-08-20

A complete solution for your training data problem with fast labeling tools, human workforce, data management, a powerful API and automation features.

  • neptune.ai Landing page
    Landing page //
    2023-08-24

Track and version your notebooks Log all your notebooks directly from Jupyter or Jupyter Lab. All you need is to install a Jupyter extension.

Manage your experimentation process Neptune tracks your work with virtually no interference to the way you like to do it. Decide what is relevant to your project and start tracking: - Metrics - Hyperparameters - Data versions - Model files - Images - Source code

Integrate with your workflow easily Neptune is a lightweight extension to your current workflow. Works with all common technologies in data science domain and integrates with other tools. It will take you 5 minutes to get started.

Labelbox

$ Details
freemium
Platforms
Browser
Release Date
-

neptune.ai

Website
neptune.ai
$ Details
freemium
Platforms
Python
Release Date
2018 April
Startup details
Country
Poland
State
Mazowieckie
City
Warsaw
Founder(s)
Piotr Niedzwiedz
Employees
10 - 19

Labelbox videos

Review App : Labelbox

More videos:

  • Review - Machine Learning Support Engineer at Labelbox
  • Review - Bounding box annotation with Labelbox

neptune.ai videos

Machine Learning Experiment Management with Neptune.ai - How to start

Category Popularity

0-100% (relative to Labelbox and neptune.ai)
Data Labeling
100 100%
0% 0
Data Science And Machine Learning
Image Annotation
100 100%
0% 0
Data Science Notebooks
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Labelbox and neptune.ai

Labelbox Reviews

  1. Unreliable

    Service goes down often. Very slow team. Slow support.

    🏁 Competitors: Diffgram
    👎 Cons:    Slow|Bad support

Top Video Annotation Tools Compared 2022
However, Labelbox only accepts .mp4 files into their platform, and only their most basic annotation modes have the full scope of video annotation options. When annotating videos with segmentation masks, annotators must step through each frame to view their work – there is no playback option.
Source: innotescus.io

neptune.ai Reviews

  1. Easy to use, not overdone, good for model management and collab

    Only negative is I didn't see it integrated with Azure, does with Google, AWS and one more. Looks real nice, and pretty powerful and plenty useful features for a data science group

Social recommendations and mentions

Based on our record, neptune.ai should be more popular than Labelbox. It has been mentiond 22 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Labelbox mentions (8)

  • Ask HN: Who is hiring? (October 2022)
    Labelbox | Remote | Frontend / WebGL, Backend, Engineering Managers | https://labelbox.com Labelbox is building the training data platform to power breakthroughs in machine learning. We provide an end to end solutions for the full AI lifecycle from creating catalogs of unstructured data all the way to building the tools for humans to label the data to teach machines. Why choose us? - Source: Hacker News / over 1 year ago
  • Model Assisted Labeling using Label box
    Hey, I have currently developed a U-Net model for segmentation and I am trying to use the model assisted labeling feature on LabelBox to annotate some masks, so I can save time on relabeling. I am just wondering if anyone is familiar with this feature or can give me a step by step guideline on how to go about doing this. I went through the examples on their GitHub but I’m honestly still very confused. Any help... Source: almost 2 years ago
  • What MDR is doing: a Machine Learning perspective
    By now, I hope you see where I'm going with this. What is MDR doing? They're creating the labelled data used to train severance chips. They get a raw download of human brains in encoded format, and go about manually labelling the different pieces based on their most basic elements. Then, based on this manually labelled data, an algorithm can be trained to create a severance chip. MDR is basically Labelbox for... Source: about 2 years ago
  • [D] Any recommendations for image annotation software .
    LabelBox - they provide free versions for research. Source: about 2 years ago
  • Video box annotation tool for Google Cloud Video Intelligence autoML CSV format ?
    Doing some progress, labelbox.com allows me to do the Video annotation, and access all data through python SDK/API... Working on converting myself to CSV GCP format :-). Source: over 2 years ago
View more

neptune.ai mentions (22)

  • A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev
    Neptune.ai - Log, store, display, organize, compare, and query all your MLOps metadata. Free for individuals: 1 member, 100 GB of metadata storage, 200h of monitoring/month. - Source: dev.to / 4 months ago
  • Show HN: A gallery of dev tool marketing examples
    Hi I am Jakub. I run marketing at a dev tool startup https://neptune.ai/ and I share learnings on dev tool marketing on my blog https://www.developermarkepear.com/. Whenever I'd start a new marketing project I found myself going over a list of 20+ companies I knew could have done something well to “copy-paste” their approach as a baseline (think Tailscale, DigitalOCean, Vercel, Algolia, CircleCi, Supabase,... - Source: Hacker News / 8 months ago
  • How to structure/manage a machine learning experiment? (medical imaging)
    There are a lot of tools out there for experiment tracking (eg neptune.ai), but I'm really not sure whether that sort of thing is over the top for what I need to do. Source: 9 months ago
  • How to grow a developer blog to 3M annual visitors? with Jakub Czakon (Neptune.ai)
    Welcome to another episode of The Developer-led Podcast, where we dive into the strategies modern companies use to build and grow their developer tools. In this exciting episode, we're joined by Jakub Czakon, the CMO at Neptune.ai, a startup that assists developers in efficiently managing their machine-learning model data. Jakub is renowned not only for his role at Neptune.ai but also for his developer marketing... - Source: dev.to / 9 months ago
  • [D] Is there any all in one deep learning platform or software
    Tbh I have done a pretty good search on this topic, I couldn't find any. I thought maybe community could help me find one, if people like you (who works at neptune.ai) have the same opinion then it is what it is :). Anyway thank you for the suggestions that you gave, probably gonna use that. Source: 11 months ago
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What are some alternatives?

When comparing Labelbox and neptune.ai, you can also consider the following products

V7 - Pixel perfect image labeling for industrial, medical, and large scale dataset creation. Create ground truth 10 times faster.

Comet.ml - Comet lets you track code, experiments, and results on ML projects. It’s fast, simple, and free for open source projects.

Playment - Playment is a fully-managed solution offering training data for AI, transcription, data collection and enrichment services at scale.

Weights & Biases - Developer tools for deep learning research

CloudFactory - Human-powered Data Processing for AI and Automation

Algorithmia - Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone.