- Core Platform
- AI Datastore Query Syntax Reference
- Annotate Many Media types: Image, Video, 3D, Text, Geo, Audio, Multi-Modal and more Annotation Summary
- Development System
- User Friendly
- Deep Data Lake Feature Store (Data Catalog)
- Unlimited Annotations and Automations
- Unlimited API & Python SDK/CLI Access
- Auto-Annotate (ML Automation)
- Automated task distribution
- Collaboration across teams
- Performance dashboards
- Robust QA & collaboration capabilities
- Model-assisted labeling (Pre-Label)
- All special tools (Video segmentation, medical, tiled)
- All Attributes and Geometric Templates
- Project Management & Data Curation
- Data Explorer (model diagnostics)
- Role Based Access Control (RBAC)
- Long 4k Videos & Ultra-High Resolution
- DICOM Native (Coming Soon)
- All Cloud Integrations (AWS, GCP, Azure)
- Issues & Comments
- Identity (OIDC, SAML, SSO, LDAP, more)
- Compliance (SOC 2, GDPR, NIST, more)
- Support: Quality Docs, Global Slack Community
- Streamlined Annotation UI suitable both from "First Time" Subject Matter Experts, and powerful options for Professional Full Time Annotators
- Many User Labeling - Designed for many users from Day 1.
- Scale to Mega Projects with sophisticated organizational concepts.
- Ingest prediction data with less scripts.
- Import Wizard saves you hours having to map your data (pre-labels, QA, debug etc.).
- All-Cloud Integrated File Browser
- Scalable pipeline for massive ingestion - we have tested to 600+ hardware nodes
- Integrated pipeline hooks - newly added data auto creates tasks and more
- Collaboration across teams between machine learning, product, ops, managers, and more.
- Store virtually any scale of dataset and instantly access slices of the data to avoid having to download/unzip/load.
- Fast access to datasets from multiple machines. Have multiple Data Scientists working on the same data.
- Integrates with your tools and 3rd party workforces.
It's a database for your training data, both metadata and access of raw BLOB data (over top of your storage choice).
- Quality Assurance Features Summary
- Webhooks with Actions
- Continually upgrade your data, including easily adding more depth
to existing partially annotated sets.
- Skip downloading and unzipping massive datasets. Explore data instantly through the browser.
Try it now (click Dataset Explorer)
- Automatic Dataset Versioning and user definable datasets.
- Collaborate share and comment on specific instances with a Diffgram Permalink.
- Compare model results visually
- Curate data and send for labeling with one click
- Uncover bad data and edge cases. Use your models to debug the human. Visually see errors.
Stream Training Data is Now Available.
- Colab Notebook Example || Docs Pytorch Tensorflow
- Make HTTP requests to your model based on user events
- Runs on your local system or cloud. Less lag, more secure, more control.
- Enforce PII & RBAC automatically across life-cycle of
training data from ingest to dataset to model predictions and back again
- Task features can be used as modules within Workflow
- Automatic Per Task Review Routing, with configurable review chance
- Advanced queue customization
- Multiple workspaces
- Enterprise dashboards
- Audit & Security Reports
The free playground version that's hosted on diffgram.com is meant to try it out and has these limits:
- Up to 2 users.
- Up to 100 files per dataset.
- Video up to 100 frames.
|Who is responsible?||You||Joint|
|How can I communicate with the diffgram team?||General community channels.||Email, Dedicated slack channel, Video|
2-3 business hours or next business day
Video calls available
For Community support, we see you as collaborator, and we may ask for a complete reproduceable example, specific technical error messages, and may even ask you to propose your own solution.
Updated 2 months ago