Software Alternatives & Reviews

CrowdFlower VS Playment

Compare CrowdFlower VS Playment and see what are their differences

CrowdFlower logo CrowdFlower

Enterprise crowdsourcing for micro-tasks

Playment logo Playment

Playment is a fully-managed solution offering training data for AI, transcription, data collection and enrichment services at scale.
  • CrowdFlower Landing page
    Landing page //
    2019-01-26
  • Playment Landing page
    Landing page //
    2023-07-22

CrowdFlower

Categories
  • AI
  • Image Annotation
  • Data Labeling
  • Data Science And Machine Learning
Website crowdflower.com
Pricing URL-

Playment

Categories
  • Data Labeling
  • Image Annotation
  • AI
  • Training Data
Website playment.io
Pricing URL Official Playment Pricing

CrowdFlower videos

How to Work on Figure Eight Tasks | How to work on crowdflower tasks | How to work on appen tasks

More videos:

  • Tutorial - How to work on Figure Eight Task | Earned 5$ in 15 mins | Easy Crowdflower Figure Eight Task

Playment videos

EARN 💲20 PER DAY BY PLAYMENT APP |WITH PAYMENT PROOF|

More videos:

  • Review - Playment : Polygon Tool Training
  • Demo - Playment for User Generated Content(UGC) Moderation Demo

Category Popularity

0-100% (relative to CrowdFlower and Playment)
Image Annotation
44 44%
56% 56
Data Labeling
40 40%
60% 60
Tool
100 100%
0% 0
AI
33 33%
67% 67

User comments

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What are some alternatives?

When comparing CrowdFlower and Playment, you can also consider the following products

Amazon Mechanical Turk - The online market place for work.

Labelbox - Build computer vision products for the real world

Universal Data Tool - Machine learning, data labeling tool, computer vision, annotate-images, classification, dataset

CloudFactory - Human-powered Data Processing for AI and Automation

Supervisely - Supervisely helps people with and without machine learning expertise to create state-of-the-art...

Labeling AI - Labeling AI is a deep learning-based auto labeling solution that develops and auto-labels custom AI by learning minimal manual labeling data.