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I'm experimenting with using SAM2 for automatic tracking of NBA players; it's mind-blowing how well this model performs out of the box. we might even add a SAM2-based tracker to the trackers package at some point. trackers:

293,362 görüntüleme • 1 yıl önce •via X (Twitter)

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SkalskiP profil fotoğrafı
SkalskiP1 yıl önce

I found this SAM2 fork that addresses my main issue with the original implementation—it allows running SAM2 on video streams!

SkalskiP profil fotoğrafı
SkalskiP1 yıl önce

the results aren’t perfect every time, but a cool idea would be to use SAM2 for automatic labeling of datasets for object detection, instance segmentation, or tracking.

SkalskiP profil fotoğrafı
SkalskiP1 yıl önce

if you want to learn more about SAM2 and how to use it for image and video segmentation, here’s a link to my stream from a few months ago.

Brandon Tyler profil fotoğrafı
Brandon Tyler1 yıl önce

So cool! Looking for stats analytics for basketball. I use @veotechnologies but they don’t offer basketball and the ai teaching is so expensive

SkalskiP profil fotoğrafı
SkalskiP1 yıl önce

@veotechnologies stay tuned I’ll be dropping more demos over the next few weeks

phillpafford profil fotoğrafı
phillpafford1 yıl önce

This is awesome, any volleyball efforts?

SkalskiP profil fotoğrafı
SkalskiP1 yıl önce

not from me… but I saw some people on X playing with volleyball usecases

HiFi-Ai / Tom Laroc profil fotoğrafı
HiFi-Ai / Tom Laroc1 yıl önce

@ImgMotion

uɐɥdǝʇS profil fotoğrafı
uɐɥdǝʇS1 yıl önce

yeah it’s bananas

Darin profil fotoğrafı
Darin1 yıl önce

Can you segment each player at the joints?

SkalskiP profil fotoğrafı
SkalskiP1 yıl önce

what to you mean by „joints”?

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"Aydan why don't you make videos anymore?" Why? Because over the last two years every single time I get in vr to try and film. This shit happens. It makes it so fucking hard to get motivated to do it when I can't get one locked off angle shot. Before you say something like "Have you covered all the reflective surfaces?" "Have you re-paired the trackers?" "Try replacing the lighthouses?" "Try replacing the cables?" "The Dongles might be bad?" "The Trackers might be bad?" "The Controllers might be bad?" "Try a USB hub?" "Try USB Ports?" "Try this 7-1 in one dongle hub?" "Try Tundra Trackers?" "Try This 3-1 dongle hub?" "Try pairing one controller each to a dongle, and pair the trackers to a single dongle too?" "Have you tried reinstalling the software? "Have you tried Steam VR Beta?" "Try Slime trackers?" "Try Quest Pro?" "Try less 2.4ghz band devices?" "Maybe your Wifi bulbs are interfering?" "Try Bigscreen Beyond 2e?" "Try 2 Lighthouses?" "Try 3 Lighthouses?" "Try 4 Lighthouses?" "Maybe your computers bad? Get a new one?" YES. I have fucking tried all that shit. Literally have spent thousands of dollars trouble shooting this issues. I currently own 3 tracking headsets 9 lighthouses 6 vive trackers 3 tundra trackers 8 slime trackers 5 pairs of index controllers. And I've tried on 3 different computers. I have talked to like 20 different people. No one has an answer. I'm at the end of my rope at this point. Every time I get into VR I'm filled with dread because I know I'm going to get mad or frustrated. I'm just...sad. I want to make content, but I don't fucking know what to do at this point.

Aydan

13,047 görüntüleme • 7 ay önce

🚀 The Segment Anything Model (SAM) has been upgraded to SAM2, featuring an efficient image encoder for segmenting images and videos. But does SAM2 outperform SAM1 in medical image and video segmentation? We're thrilled to present our paper "Segment Anything in Medical Images and Videos: Benchmark and Deployment"! We comprehensively benchmark SAM2 across 11 medical image modalities and videos. 📄 Paper: 💻 Code: **Highlights:** 1. SAM2 doesn’t always outperform SAM1 in 2D medical images, but excels in video segmentation, making it more accurate and efficient for 3D images, such as CT and MR scans. 2. MedSAM still outperforms SAM2 on most 2D modalities, but SAM2 surpasses MedSAM for 3D image segmentation in a slice-by-slice approach. 3. Segmentation performance varies with model size; sometimes the smallest model outperforms larger ones. 4. Fine-tuning SAM2 significantly boosts its performance for medical image segmentation. While SAM2 may struggle with challenging objects that have unclear boundaries or low contrast, it excels in generating good initial segmentation masks for common medical images and videos. However, the official interface doesn’t support medical data formats and has limitations on video length. To address this, we've developed a 3D Slicer Plugin and Gradio API for efficient 3D medical image and video segmentation. We invite you to try them out and provide feedback! 🔧 Deployment: - 3D Slicer Plugin: - Gradio API: (Note: Due to GPU limitations, the online API is available for only 12 hours and may be slow. We highly recommend deploying the Gradio API with your own computing resources: A big shoutout to Jun Ma (JunMa) who recently joined our UHN AI hub (UHN AI Hub) as Machine Learning Lead, and kudos to all co-authors: Sumin Kim, Feifei Li, Mohammed Baharoon (Mohammed Baharoon), Reza Asakereh, and Hongwei Lyu! This is true teamwork! Looking forward to collaborating with the community to advance 3D medical image and video segmentation foundation models! University Health Network U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology Temerty Centre for AI in Medicine (T-CAIREM) Vector Institute #MedTech #AIinHealthcare #DeepLearning #MedicalImaging #SAM2 #MedSAM #AIResearch

Bo Wang

178,791 görüntüleme • 2 yıl önce