SAM 2 | Segment Anything Model 2
Meta AI has released Segment Anything 2 (SAM 2), an advanced image and video segmentation foundation model. SAM 2 allows users to input points in an image to generate segmentation masks for those points, and it can also generate and track segmentation masks across frames in a video.
Segment Anything 2 (SAM 2) by Meta AI is open-source. It is a follow-up to the original Segment Anything Model (SAM) and is designed to enable zero-shot segmentation of objects in images.
Building on the original SAM model released by Meta last year, SAM 2 has been utilized for various vision applications, including image segmentation and as an image labeling assistant. Meta reports that SAM 2 is six times more accurate than its predecessor in image segmentation tasks.
In this guide, we will explore what Segment Anything 2 is, how it functions, and how you can leverage the model for image segmentation tasks.
Steps to run SAM 2:
conda create -n samm python=3.12
conda activate samm
git clone https://github.com/facebookresearch/s...
cd segment-anything-2
python setup.py build_ext --inplace
%cd checkpoints
double click on it. Model checkpoints will be downloaded in your directory.
Then install this- we need this to us the SAM 2 predictor and run the example notebooks,
pip install --no-build-isolation -e ".[demo]"
conda install jupyter notebook
jupyter notebook