Meta's Segment Anything written with C++ / GGML

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

Well... damn. Is there a framework like this (or this directly?) which can run object detection? People, car types, makes, animals, etc?

I am looking for a model similar to this, but for text. I want to group text with different labels that apply to subsets of the text. Think of being able to quickly pull-out related segments from a large body of text. Let's take, for instance, a sales contract that specifies a discounted price for various goods. If you select the label "data rows", the system should be able to extract all the text pertaining to the table that specifies which SKUs are being purchased, and at what discounted price. Moreover, this model should be capable of segmenting the content into semantically relevant chunks. One example: each row in the aforementioned table would be tagged with multiple labels. One would be just that it is a row, the data in the first column should be labeled for what it represents, e.g. "product number". Another example: if there's a section discussing the terms of delivery or warranty conditions, selecting the respective labels would instantly extract that specific information, regardless of where it's located within the document. Would be great for it to be able to segment into some controllable range of tokens/characters to allow for pulling those chunks into a vector database, along with the relevant tags related to the chunk.

Big fan of your work GGML friends

Hmm wonder how this compares to stuff like FastSAM and MobileSAM. Is SAM quantized better or are those knock of architectures more performant.

Bravo, the demonstration is genuinely impressive!<p>Next Step: Incorporate this library into image editors like Photopea (via WebAssembly) to boost the speed of common selection tasks. The magic wand is a tool of the past.<p>I&#x27;d pay for such a feature.

While I love the efficiency from these Python to C++ ports I can&#x27;t stop thinking about the long tail of subtle bugs that will likely infest these libraries forever but then the Python versions also sit atop C&#x2F;C++ cores

I&#x27;m so glad the AI community is finally starting to ditch Python. It has held progress back for far too long.