superb_hq's profile picture. End-to-end training data platform that automates data preparation at scale and makes building datasets quick, systematic and repeatable. Backed by @ycombinator

Superb AI

@superb_hq

End-to-end training data platform that automates data preparation at scale and makes building datasets quick, systematic and repeatable. Backed by @ycombinator

Announcing the integration of NVIDIA AI Blueprint for video search and summarization into our Video Analytics. This marks a significant milestone in AI-driven video processing, delivering AI agents that parse video data through natural language commands. 👉hubs.li/Q03bMSHY0


Superb AIの強力なツール! 第2弾は「スナップ、結合、減算」 重なるポリゴンをスナップし、自由にポリゴンを結合し、簡単に一つのポリゴンから別のポリゴンを簡単に減算します。 詳細はこちら👉【hubs.li/Q0341rpp0#SuperbAI #ビジョンAI #アノテーション #ツール #AI #便利なツール

superb_hq's tweet image. Superb AIの強力なツール!
第2弾は「スナップ、結合、減算」
重なるポリゴンをスナップし、自由にポリゴンを結合し、簡単に一つのポリゴンから別のポリゴンを簡単に減算します。
詳細はこちら👉【hubs.li/Q0341rpp0】
#SuperbAI #ビジョンAI #アノテーション #ツール #AI #便利なツール

Want to build and deploy computer vision applications as easily as mobile apps? That's why we built Superb Model. Teams can now bring the full model lifecycle into one seamlessly integrated workflow. Read our Model launch blog to find out more! hubs.li/Q020Lzp20


We have stayed steady on our mission to democratize AI. With the release of our 3rd module, Superb Model, we are inching closer to that reality. Hear from our CEO, Hyun Kim, as he reflects on the journey and outlines what's to come! hubs.li/Q020zH370


Great article from @rasbt on scaling PyTorch model training. 🤓 He dives into mixed-precision techniques and multi-GPU training paradigms using a simple Vision Transformer (ViT) trained to classify images as a base model. hubs.li/Q01XR9Xt0


Take a peak behind the curating and check out Superb Curate's sample scatter view capabilities within our clustering algorithm. #computervision #modeltraining #machinelearning #AI


🗺️ Python package for segmenting #GeospatialData with the Segment Anything Model (SAM). The repo's neat organization and insightful video tutorials are a plus! Kudos to @giswqs for this resource! 👉 hubs.li/Q01XR1y40


Navigating the world of object detection models?🚀 It's not just about snagging the highest performer! Check out our tips on Ground Truth to find the perfect fit for your project. Drop by and share your own #ObjectDetection strategy. hubs.li/Q01YJRR50

superb_hq's tweet image. Navigating the world of object detection models?🚀 It's not just about snagging the highest performer! Check out our tips on Ground Truth to find the perfect fit for your project. Drop by and share your own #ObjectDetection strategy. 
hubs.li/Q01YJRR50

Thankful to have our blog featured by AI Infrastructure Alliance! Check it out 👉 hubs.li/Q01YJR750 #


An awesome visual and interactive explainer of Convolutional Neural Networks' inner workings and operations. 😍 hubs.li/Q01XmGzD0


In the world of #AI, model building often outshines data annotation. Yet, it's an ongoing, vital task that underpins AI success. But who's handling this less glamorous job? @verge's article unravels the complex supply chain of human annotation services. hubs.li/Q01Xd7qL0

theverge.com

Inside the AI Factory

How many humans does it take to make tech seem human? Millions.


Labeling doesn't get any easier! 😎 Our SAM-based Auto Edit, which supports mouse hover labeling. Take labeling automation to the next level with AutoEdit, which offers higher labeling speeds, improved accuracy & usability, and provides more details. ➡️hubs.li/Q01Ydw3Z0


With Superb Label's Custom Auto-Label Technology, ioCrops Labels 90,000 Instances in Just 8 Days - averaging roughly 75 seconds per label! Read the full Case Study here ➡️hubs.li/Q01XRf_V0 #machinelearning #computervision #AI #casestudy #tech #agritech


MobileSAM is a lightweight version of the Segment Anything Model. Performance is on par with the original SAM but with a smaller Tiny-ViT encoder, it runs at 12ms per image on a single GPU. 🚀 hubs.li/Q01XsnrQ0 #ComputerVision

superb_hq's tweet image. MobileSAM is a lightweight version of the Segment Anything Model. Performance is on par with the original SAM but with a smaller Tiny-ViT encoder, it runs at 12ms per image on a single GPU. 🚀 hubs.li/Q01XsnrQ0
#ComputerVision

With Superb Label, benefit from building your own 3D datasets & validating efficient 3D model training with precision and accuracy that can't be solved with 2D models. Read about it: hubs.li/Q01XHqG-0 #SuperbAI #Lidar #computervision #machinelearning #AI #3DModeling


⚡ Exciting news for ML developers! Keras 3.0 beta version is out! This version makes it possible to run Keras workflows on top of arbitrary frameworks - starting with TensorFlow, JAX, and PyTorch. 😍 @fchollet hubs.li/Q01XFphH0


🌟 Excited to be a part of our customer's success stories! ✨ Edge Vision approached us after manual labeling drained time and resources from their in-house ML team. Read their Story ➡️ hubs.li/Q01Xnfh_0 #SuperbAI #ComputerVision #SuccessStories #MachineLearning

superb_hq's tweet image. 🌟 Excited to be a part of our customer's success stories! ✨

Edge Vision approached us after manual labeling drained time and resources from their in-house ML team.  Read their Story ➡️ hubs.li/Q01Xnfh_0

#SuperbAI #ComputerVision #SuccessStories #MachineLearning
superb_hq's tweet image. 🌟 Excited to be a part of our customer's success stories! ✨

Edge Vision approached us after manual labeling drained time and resources from their in-house ML team.  Read their Story ➡️ hubs.li/Q01Xnfh_0

#SuperbAI #ComputerVision #SuccessStories #MachineLearning

💌 Hot off the press: Computer Vision Newsletter No.41 is live on Ground Truth! Highlights include 'State of Computer Vision 2023', a visual guide to CNNs, accelerating PyTorch training, and more. Subscribe to keep up with #ComputerVision hubs.li/Q01XlyxG0


At Superb AI, we love seeing how #AI is changing the way we all do business across multiple disciplines. Discover how DeepL's cutting-edge #technology is transforming the translation landscape, breaking barriers and bridging global communication gaps. 👉hubs.li/Q01Xd3Tt0


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