Data Labeler Revenue and Competitors
Estimated Revenue & Valuation
- Data Labeler's estimated annual revenue is currently $6.7M per year.
- Data Labeler's estimated revenue per employee is $144,685
Employee Data
- Data Labeler has 46 Employees.
- Data Labeler grew their employee count by -6% last year.
Data Labeler's People
Name | Title | Email/Phone |
---|
Data Labeler Competitors & AlternativesAdd Company
Competitor Name | Revenue | Number of Employees | Employee Growth | Total Funding | Valuation |
---|---|---|---|---|---|
#1 | $0.6M | 17 | -6% | $5.5M | N/A |
#2 | $8.7M | 67 | 5% | N/A | N/A |
#3 | $17.8M | 123 | 26% | N/A | N/A |
#4 | $56.5M | 307 | 7% | $132.3M | N/A |
#5 | $36.8M | 231 | 3% | N/A | N/A |
#6 | $2.6M | 24 | -4% | N/A | N/A |
#7 | $1483.3M | 7307 | 27% | N/A | N/A |
#8 | $19.9M | 137 | 11% | N/A | N/A |
#9 | $2.6M | 20 | -20% | $4M | N/A |
#10 | $68.7M | 362 | -1% | N/A | N/A |
What Is Data Labeler?
Data Labeler specializes in providing reliable and high-quality training data sets for Machine Learning/Artificial Intelligence initiatives. With our innovative & efficient data annotation & labeling services, we help companies to accelerate AI development & significantly reduce time to market. Vision – To provide the highest quality training data sets using a knowledgeable workforce. Mission – To provide a pivotal service that allows companies to focus on their core (AI/ML) business, while we create the data sets that you need to power your algorithms. Data Annotations We Support: Bounding Boxes for Object Detection Polygons for Semantic and Instance Segmentation Points for Facial Recognition and Body Pose Detection Texts for Image Captioning Select & Multi-Select for Image Classification Our teams are working around the clock 24/7 to give our clients the highest quality labeled data as fast as possible.
keywords:N/AN/A
Total Funding
46
Number of Employees
$6.7M
Revenue (est)
-6%
Employee Growth %
N/A
Valuation
N/A
Accelerator
Company Name | Revenue | Number of Employees | Employee Growth | Total Funding |
---|---|---|---|---|
#1 | $15.7M | 46 | 18% | N/A |
#2 | $7.5M | 46 | 10% | N/A |
#3 | $11M | 48 | 0% | N/A |
#4 | $16.4M | 50 | -6% | N/A |
#5 | $7.5M | 52 | 8% | N/A |