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features
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Description

Amazon SageMaker enables the identification of various types of unprocessed data, including images, text documents, and videos, while also allowing for the addition of meaningful labels and the generation of synthetic data to develop high-quality training datasets for machine learning applications. The platform provides two distinct options, namely Amazon SageMaker Ground Truth Plus and Amazon SageMaker Ground Truth, which grant users the capability to either leverage a professional workforce to oversee and execute data labeling workflows or independently manage their own labeling processes. For those seeking greater autonomy in crafting and handling their personal data labeling workflows, SageMaker Ground Truth serves as an effective solution. This service simplifies the data labeling process and offers flexibility by enabling the use of human annotators through Amazon Mechanical Turk, external vendors, or even your own in-house team, thereby accommodating various project needs and preferences. Ultimately, SageMaker's comprehensive approach to data annotation helps streamline the development of machine learning models, making it an invaluable tool for data scientists and organizations alike.

Description

LabelMe aims to offer an online platform for annotating images, facilitating the creation of image databases for research in computer vision. By utilizing the annotation tool, users can actively contribute to the growing database. Images can be systematically organized into collections, with the flexibility to create nested collections akin to folders. When a user downloads their database, the organization of collections will reflect this folder structure. Users can also upload images to their collections and annotate them using the LabelMe tool. Furthermore, unlisted collections allow for viewing by anyone with access to the specific URL, although they won't be featured among public folders. Ultimately, LabelMe's objective is to ensure that both images and annotations are made accessible to the research community without any limitations, fostering collaboration and innovation. This commitment to open access highlights the importance of shared resources in advancing computer vision research.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon SageMaker Yes 
Amazon SageMaker Unified Studio Yes 
ZenML Yes 

Integrations

Amazon SageMaker No 
Amazon SageMaker Unified Studio No 
ZenML No 

Pricing Details

$0.08 per month
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Amazon Web Services

Founded

2006

Country

United States

Website

aws.amazon.com/es/sagemaker/data-labeling/

Vendor Details

Company Name

LabelMe

Website

labelme.csail.mit.edu/Release3.0/

Product Features

Data Labeling

Human-in-the-loop No 
Labeling Automation No 
Labeling Quality No 
Performance Tracking No 
Polygon, Rectangle, Line, Point No 
SDK No 
Supports Audio Files No 
Task Management No 
Team Collaboration No 
Training Data Management No 

Product Features

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