Data entry ops · Production

Labelbox helps Move.ai iterate 2x faster on computer vision algorithms with video annotation

The problem

Move.ai needed to train multiple AI algorithms for complex computer vision tasks — identifying people, classifying objects on a person such as jersey numbers, and recognizing human limbs in varied poses and distances, often occluded — across several distinct use cases, all requiring large amounts of labeled training data generated at speed.

Workflow diagram · grounded in source
1
Training data need identified
trigger
“The team turned to Labelbox to help them spin up a labeling team, develop training data for their models, and iterate — all at top speed”
2
Labeling pipeline setup
integration
“Labelbox's platform is designed to help AI teams like Move.ai quickly set up a labeling pipeline that connects seamlessly with the rest of their ML operations and trains multiple algorithms, fast”
3
Boost workforce annotates video data
human_review
“Labelbox's dedicated workforce team, Boost, also provided a high standard of accuracy and quick responses from the account team”
4
Export labeled data to ML frameworks
integration
“After annotating their data in Labelbox, the Move.ai team exports their labeled data into Tensorflow or PyTorch frameworks to train their model”
5
Model deployed in production
output
“leverages TensorRT for production. All of this is complemented with powerful GPUs for effective AI computation. The team is currently harnessing Nvidia RTX GPUs for research while using Nvidia T4s for production”
Reported outcome

Labelbox's data engine provided faster iterations on Move.ai's algorithms, helping them move 2x faster in the domain and accelerate their go-to-market strategy and subsequent product launches.

Reported metrics
Algorithm iteration speed2x as fast
Reported stack
LabelboxBoostPython SDKTensorflowPyTorchTensorRTNvidia RTX GPUsNvidia T4s
Source
https://labelbox.com/customers/move-ai-customer-story/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Labelbox's data engine provided faster iterations on Move.ai's algorithms, helping them move 2x faster in the domain and accelerate their go-to-market strategy and subsequent product launches.

What tools did this team use?

Labelbox, Boost, Python SDK, Tensorflow, PyTorch, TensorRT, Nvidia RTX GPUs, Nvidia T4s.

What results were reported?

Algorithm iteration speed: 2x as fast (source-reported, not independently verified).

How is this data entry ops AI workflow structured?

Training data need identified → Labeling pipeline setup → Boost workforce annotates video data → Export labeled data to ML frameworks → Model deployed in production.

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