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Open source datasets & models https://t.co/IzwgoIkBEeroboflow.comJoined June 2019
supervision is #3 trending repo today and crossed 40k stars a few days ago, we built it to make computer vision dead simple
what would make it even easier? what is slowing you down when building with vision tools?
let us know the features you want, we are working on the roadmap. need your feedback
supervision just hit 40,000 GitHub stars!
it now powers over 6.5k open-source computer vision projects, including all my demos like basketball AI
link: github.com/roboflow/super…
No data, no problem
introducing agentic synthetic data generation with Cosmos 3
share a few examples, generate more data, automate model training, automatically deploy the latest version with no downtime
in a benchmark run with Corning Incorporated's optical fiber manufacturing engineering team, a model trained on 8 real defect images plus synthetic examples generated by Cosmos reached 0.95 mean average precision and perfect recall on the toughest defect class, beating a baseline trained on real data alone.
"The Roboflow Agent powered by NVIDIA allows us to generate the training data we need, fine-tune our models, and strengthen model performance and inspection quality while increasing the speed, scalability, and adoption of next-generation technologies,” - Jeremy Knopf, chief information officer, Corning Optical Communications
Introducing Cosmos 3: Our latest frontier model for Physical AI
Cosmos 3 is the world’s first fully open omnimodel with native vision reasoning, world and action generation.
Today we’re releasing Super (32B) and Nano (8B) variants.
@aiseomastery From what we've seen, can be just 5 real examples. Totally depends on the environment, use case, defect types/variations, etc lots of variables
@aiseomastery depends on the type of defect but yes generally still does well
best way to operate is using active learning to continually update the dataset with new examples
@NVIDIAAI Tested NVIDIA's new Cosmos 3 myself. Easy tier first: zero-shot gate turnaround.
Single inference of 1.5min video, 0.5fps ingestion, 6 segmented states. Thinking on, no fine-tuning, prompted it to focus on cargo in the cropped ROI.
Harder tests below. 🧵
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5K Followers 3K FollowingCo-founder, CTO @roboflow: Give your software the sense of sight; use computer vision without becoming a machine learning expert.
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