This is a port of Meta's Segment Anything computer vision model which allows easy segmentation of shapes in images. Originally written in Python, Yavor Ivanov has ported it to C++ using the GGML library created by Georgi Gerganov which is optimized for CPU instead of GPU, specifically Apple Silicon M1/M2. The repo is still in it's early stage
Do you know how the time to do the image embedding takes? In SAM, most of the time is spent generating a very expensive embedding (prohibitive for real-time object detection). From the timing on your page it looks like yours is also similarly slow, but I'm curious how it compares to the pytorch Meta implementation.
Depends on the machine, number of threads selected and the model checkpoint used (Vit-B or Vit-L or Vit-B). The video demo attached is running on Apple M2 Ultra and using the Vit-B model. The generation of the image embedding takes ~1.9s there and all the subsequent mask segmentations take ~45ms.
However, I am now focusing on improving the inference speed by making better use of ggml and trying out quantization. Once I make some progress in this direction I will compare to other SAM alternatives and benchmark more thoroughly.
