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SAM 2 from Meta FAIR is the first unified model for real-time, promptable object segmentation in images & videos. Using the model in our web-based demo you can segment, track and apply effects to objects in video in just a few clicks. Try SAM 2 ➡️

88,918 Aufrufe • vor 2 Jahren •via X (Twitter)

10 Kommentare

Profilbild von AshutoshShrivastava
AshutoshShrivastavavor 2 Jahren

SAM 2 is really awesome, was really surprised to see how it tracked fast moving object.

Profilbild von Anu Aakash
Anu Aakashvor 2 Jahren

sam2 is so cool

Profilbild von Alexandre Devaux
Alexandre Devauxvor 2 Jahren

Super impressive 😀 I got a cool idea if we can run SAM2 in the Browser or through an API:

Profilbild von MindBranches
MindBranchesvor 2 Jahren

So cool! Summary of the Meta SAM 2 announcement:

Profilbild von Dale Carman
Dale Carmanvor 2 Jahren

it says access denied when you click the link

Profilbild von Tianjian Cai
Tianjian Caivor 2 Jahren

Why post again? I think it has already been posted.

Profilbild von 𖤓🪄
𖤓🪄vor 2 Jahren

Unfortunately, there are no devices to use all of this on

Profilbild von AI Furry Art (SFW-ish)
AI Furry Art (SFW-ish)vor 2 Jahren

Is there a version of this exact UI that can run locally? I know some not so tech savvy people who would get good use out of it.

Profilbild von hf
hfvor 2 Jahren

It's very useful.

Profilbild von aitization 𝕏 
aitization 𝕏 vor 2 Jahren

cool

Ähnliche Videos

Everyone is sleeping on Meta's SAM 3 release. But it's actually a big deal. Here's why: Companies spend millions paying humans to label images and videos frame by frame. A single autonomous driving dataset? Months of work, hundreds of annotators, millions in cost. Without labeled data, you can't train custom models. Without custom models, you're stuck with generic solutions. This is why most companies never move past pilots. SAM 3 breaks this cycle. First let's look at the evolution: SAM 1 segmented objects when you clicked on them. Revolutionary, but one object at a time. SAM 2 added video tracking with memory. Game-changing, but you still manually prompted every object. SAM 3 changes everything with text prompts. Type "yellow school bus" and it finds ALL of them in your image or video. Not just one. Every instance across thousands of frames. Now here's where people get confused: "Can't I just use GPT-5 or Gemini for this?" No, and here's why that's a terrible approach. Large multimodal LLMs are great for reasoning, but they're slow and expensive for production visual tasks. You're paying API costs per image, waiting seconds for responses, getting inconsistent results. SAM 3 runs in 30 milliseconds on a single GPU for 100+ objects. That's 100x faster, and you own the infrastructure. More importantly, SAM 3 gives you precise pixel-level masks, not descriptions. Try asking an LLM to segment every defective part on a manufacturing line in real-time. It won't work. SAM 3 does this effortlessly. The real breakthrough is their data engine. Meta built an AI-human hybrid system that's 5x faster for complex annotations. They trained SAM 3 on 4 million unique visual concepts - 50x more than existing benchmarks like LVIS. SAM 3 is trained on 4 million unique visual concepts, it handles everything: - Text-based concept search - Interactive refinement with clicks - Video tracking across frames - Zero-shot detection of new concepts The model is open source. Weights, code, and benchmarks are on GitHub. If you're building computer vision applications, this is the foundation model to evaluate. The annotation time savings alone will pay for integration costs within weeks. Find the relevant links in the next tweet!

Akshay 🚀

46,438 Aufrufe • vor 9 Monaten