Labelbox Vs Diffgram

Top Reasons to Choose Diffgram over Labelbox

1. Diffgram is more flexible for your business needs, see: Development System.
2. Diffgram gives you better ROI with Unlimited usage
3. Diffgram includes virtually all functions of Labelbox.

Mapping of Labelbox Areas to Diffgram

Diffgram covers nearly all functions that Labelbox does (and gives you some new ones). This table has the area, description of the area, and link to related Diffgram page.

One click migration

Migrate legacy Labelbox projects to Diffgram

A better way to build AI

"Improve model performance through fast and impactful data-centric iterations. Find the data that will boost model performance using active learning and model error analysis. Save time and money by focusing resources where your specific model needs the most help."

Yes you can do this with Diffgram too.

Further, for some use cases, you can automatically do this by more deeply engaging your existing end users. See Embed

Label data faster than ever

"Achieve up to 80% in labeling efficiency gains with model-assisted labeling – use models to pre-label data, and let humans focus on corrective actions to generate ground truth so they don’t need to start from scratch."

Yes you can do this with Diffgram too.

However, we have been observing over time that this can create a bad feedback loop. Workflow can help surface these automations and add monitoring steps to better understand those feedback loops. Don't just blindly automate, automate smartly with Diffgram.

Workflow

In Labelbox, Workflow is focused around Annotations only. In Diffgram, Workflow has a broader scope. In Diffgram you can have a series of human annotation blocks (e.g. review steps) and non-linear tasks flows (through task definitions) in a similar way. Additionally, in Diffgram Workflow can also include other blocks, like model training, pre-labeling, export, web-hooks, etc.

Data Engine

Labelbox and Diffgram are both Data Engines. And solve similar problems as outlined by the Data Engine Guide:

"AI teams and enterprises who don’t already have the time and resources to architect an
intricate and complex data engine for every use case, however, often face a slow and arduous
journey toward production AI, with common roadblocks that include:
• Siloed AI efforts that create multiple versions of ground truth, duplicate data, and other
challenges
• A large number of stakeholders working within a system that lacks efficient collaboration
tools and easy visibility into analytics
• Team members who want to speed up their projects and build temporary solutions for
data processing and labeling, creating broken processes in the long term
• Poorly built data engines that aren’t scalable and don’t meet requirements for active
learning techniques, labeling operations, model error analysis, and more"

"Teams that purchase data engine software gain significant benefits (besides not having to
build their own infrastructure), including:
• The flexibility to use it for any AI project, even if they require entirely different modalities
of data, labeling requirements, and training techniques
• The ability to use pre-built solutions for data curation (integral to active learning),
labeling automation, and model evaluation and training
• The ability to work with any internal, external, or combination of labeling
teams and vendors
• Support for any labeling operations, workflow, or other software issue"

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Comparison Article

Labelbox vs Diffgram Comparison