This Facebook AI model is the CHATGPT of Computer Vision (with Python Code)
From Meta AI blog:
"Segmentation — identifying which image pixels belong to an object — is a core task in computer vision and is used in a broad array of applications, from analyzing scientific imagery to editing photos. But creating an accurate segmentation model for specific tasks typically requires highly specialized work by technical experts with access to AI training infrastructure and large volumes of carefully annotated in-domain data.
Today, we aim to democratize segmentation by introducing the Segment Anything project: a new task, dataset, and model for image segmentation, as we explain in our research paper. We are releasing both our general Segment Anything Model (SAM) and our Segment Anything 1-Billion mask dataset (SA-1B), the largest ever segmentation dataset, to enable a broad set of applications and foster further research into foundation models for computer vision. We are making the SA-1B dataset available for research purposes and the Segment Anything Model is available under a permissive open license (Apache 2.0). Check out the demo to try SAM with your own images."
Colab Code - https://colab.research.google.com/drive/1Wp23tEsqAqL44CJRjokfjGkuCWULJgNb?usp=sharing
SAM website demo - https://segment-anything.com/demo
SAM Github - https://github.com/facebookresearch/segment-anything
Kadir Nar's SAM Python library - https://github.com/kadirnar/segment-anything-video
SAM announcement blog - https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/
❤️ If you want to support the channel ❤️
Support here:
Patreon - https://www.patreon.com/1littlecoder/
Ko-Fi - https://ko-fi.com/1littlecoder
From Meta AI blog:
“Segmentation — identifying which image pixels belong to an object — is a core task in computer vision and is used in a broad array of applications, from analyzing scientific imagery to editing photos. But creating an accurate segmentation model for specific tasks typically requires highly specialized work by technical experts with access to AI training infrastructure and large volumes of carefully annotated in-domain data.
Today, we aim to democratize segmentation by introducing the Segment Anything project: a new task, dataset, and model for image segmentation, as we explain in our research paper. We are releasing both our general Segment Anything Model (SAM) and our Segment Anything 1-Billion mask dataset (SA-1B), the largest ever segmentation dataset, to enable a broad set of applications and foster further research into foundation models for computer vision. We are making the SA-1B dataset available for research purposes and the Segment Anything Model is available under a permissive open license (Apache 2.0). Check out the demo to try SAM with your own images.”
Colab Code – https://colab.research.google.com/drive/1Wp23tEsqAqL44CJRjokfjGkuCWULJgNb?usp=sharing
SAM website demo – https://segment-anything.com/demo
SAM Github – https://github.com/facebookresearch/segment-anything
Kadir Nar’s SAM Python library – https://github.com/kadirnar/segment-anything-video
SAM announcement blog – https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/
❤️ If you want to support the channel ❤️
Support here:
Patreon – https://www.patreon.com/1littlecoder/
Ko-Fi – https://ko-fi.com/1littlecoder
