The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

This is the official code for MobileSAM project that makes SAM lightweight for mobile applications and beyond!

https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1

Segment Anything in Medical Images

Official PyTorch implementation of "EdgeSAM: Prompt-In-the-Loop Distillation for On-Device Deployment of SAM"

Combining Segment Anything (SAM) with Grounded DINO for zero-shot object detection and CLIPSeg for zero-shot segmentation

[NeurIPS2023] Code release for "Hierarchical Open-vocabulary Universal Image Segmentation"

Running segment-anything image embedding, prompting, and mask generation as torchserve services

Unofficial implementation and experiments related to Set-of-Mark (SoM) 👁️

My journey to learn and grow in the domain of Machine Learning and Artificial Intelligence by performing the #100DaysofMLCode Challenge. Now supported by bright developers adding their learnings 👍

Set-of-Mark Visual Prompting for GPT-4V