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🗓️ Remember when last April, 
🗓️ Remember when last April, 
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@Meta
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 released Segment Anything Model (SAM) paper and it was too good to be true. 🤯
 released Segment Anything Model (SAM) paper and it was too good to be true. 🤯
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They have now released Segment Anything Model 2 (SAM 2) and it's mind-blowingly great! 🚀
They have now released Segment Anything Model 2 (SAM 2) and it's mind-blowingly great! 🚀
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SAM 2 is the first unified model for segmenting objects across images and videos. You can use a click, box, or mask as the input to select an object on any image or frame of video. 🖼️📹
SAM 2 is the first unified model for segmenting objects across images and videos. You can use a click, box, or mask as the input to select an object on any image or frame of video. 🖼️📹
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SAM consists of an image encoder to encode images, a prompt encoder to encode prompts, then outputs of these two are given to a mask decoder to generate masks. 🎭
SAM consists of an image encoder to encode images, a prompt encoder to encode prompts, then outputs of these two are given to a mask decoder to generate masks. 🎭
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The biggest jump of SAM2 from SAM is using memory to have consistent masking across frames! They call it masklet prediction! 🧠
The biggest jump of SAM2 from SAM is using memory to have consistent masking across frames! They call it masklet prediction! 🧠
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They have also released the dataset, SA-V 
They have also released the dataset, SA-V 
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This dataset is truly huge, with 190.9K manual annotations and 451.7K automatic! 📊
This dataset is truly huge, with 190.9K manual annotations and 451.7K automatic! 📊
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📄 Paper: 
📄 Paper: 
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https://ai.meta.com/research/publications/sam-2-segment-anything-in-images-and-videos/
https://ai.meta.com/research/publications/sam-2-segment-anything-in-images-and-videos/
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📝 Blog: 
📝 Blog: 
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https://ai.meta.com/sam2/
https://ai.meta.com/sam2/
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🔗 Demo: 
🔗 Demo: 
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https://sam2.metademolab.com/demo
https://sam2.metademolab.com/demo
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💾 Model Weights: 
💾 Model Weights: 
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https://github.com/facebookresearch/segment-anything-2/blob/main/checkpoints/download_ckpts.sh
https://github.com/facebookresearch/segment-anything-2/blob/main/checkpoints/download_ckpts.sh
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📁 Dataset: 
📁 Dataset: 
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https://ai.meta.com/datasets/segment-anything-video-downloads/
https://ai.meta.com/datasets/segment-anything-video-downloads/
🗓️ Remember when last April, @Meta released Segment Anything Model (SAM) paper and it was too good to be true. 🤯

They have now released Segment Anything Model 2 (SAM 2) and it's mind-blowingly great! 🚀

SAM 2 is the first unified model for segmenting objects across images and videos. You can use a click, box, or mask as the input to select an object on any image or frame of video. 🖼️📹

SAM consists of an image encoder to encode images, a prompt encoder to encode prompts, then outputs of these two are given to a mask decoder to generate masks. 🎭

The biggest jump of SAM2 from SAM is using memory to have consistent masking across frames! They call it masklet prediction! 🧠

They have also released the dataset, SA-V 
This dataset is truly huge, with 190.9K manual annotations and 451.7K automatic! 📊

📄 Paper: https://ai.meta.com/research/publications/sam-2-segment-anything-in-images-and-videos/

📝 Blog: https://ai.meta.com/sam2/

🔗 Demo: https://sam2.metademolab.com/demo

💾 Model Weights: https://github.com/facebookresearch/segment-anything-2/blob/main/checkpoints/download_ckpts.sh

📁 Dataset: https://ai.meta.com/datasets/segment-anything-video-downloads/
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