microsoft/Mage-VL
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Ron
ronedgecomb
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EVERYTHING TECHNOLOGY
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We're excited to announce BananaMind 2V, our small vision model series!
These models are NOT released yet.
We will release them in mid-august!
BananaMind 2V will include:
BananaMind 2V 256M, the flagship based on BananaMind 2 Pro (BananaMind 2 Pro is not released yet).
BananaMind 2V 100M, our mid model, based on BananaMind 2 Medium.
BananaMind 2V 50M, our smallest vision model, based on BananaMind 2 Mini.
These are currently unreleased and will release in mid-august.
Our training will start after BananaMind 2 Pro has finished training.
These models are NOT released yet.
We will release them in mid-august!
BananaMind 2V will include:
BananaMind 2V 256M, the flagship based on BananaMind 2 Pro (BananaMind 2 Pro is not released yet).
BananaMind 2V 100M, our mid model, based on BananaMind 2 Medium.
BananaMind 2V 50M, our smallest vision model, based on BananaMind 2 Mini.
These are currently unreleased and will release in mid-august.
Our training will start after BananaMind 2 Pro has finished training.
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reacted to Xenova's post with 🔥 4 days ago
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28009
Okay this is insane... WebGPU-accelerated semantic video tracking, powered by DINOv3 and Transformers.js! 🤯
Demo (+ source code): webml-community/DINOv3-video-tracking
This will revolutionize AI-powered video editors... which can now run 100% locally in your browser, no server inference required (costs $0)! 😍
How does it work? 🤔
1️⃣ Generate and cache image features for each frame
2️⃣ Create a list of embeddings for selected patch(es)
3️⃣ Compute cosine similarity between each patch and the selected patch(es)
4️⃣ Highlight those whose score is above some threshold
... et voilà! 🥳
You can also make selections across frames to improve temporal consistency! This is super useful if the object changes its appearance slightly throughout the video.
Excited to see what the community builds with it!
Demo (+ source code): webml-community/DINOv3-video-tracking
This will revolutionize AI-powered video editors... which can now run 100% locally in your browser, no server inference required (costs $0)! 😍
How does it work? 🤔
1️⃣ Generate and cache image features for each frame
2️⃣ Create a list of embeddings for selected patch(es)
3️⃣ Compute cosine similarity between each patch and the selected patch(es)
4️⃣ Highlight those whose score is above some threshold
... et voilà! 🥳
You can also make selections across frames to improve temporal consistency! This is super useful if the object changes its appearance slightly throughout the video.
Excited to see what the community builds with it!
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3292
Two big projects are open sourced soon. Get ready...
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