Grounded-Segment-Anything VS CLIP

Grounded SAM is a project that tries to combine these steps in a single workflow, but I am not sure how far it has come along. Might be worth checking out, but it also isn't too difficult to combine the three models by hand.

We build a simple demo ImageBind-SAM here which aims to segment with different modalities

GroundingDINO install failed. Please submit an issue to https://github.com/IDEA-Research/Grounded-Segment-Anything/issues.

This is because C++ is somehow not compiled. Check https://github.com/IDEA-Research/Grounded-Segment-Anything/issues/53 too see whether it’s working, otherwise search through similar issues. Let me know which solution works. Remember that ‘export’ on windows is ‘set’, and you should make sure that CUDA_HOME exists in your environment variable.

I'm glad to do so after I implement this.

here is the GitHub link: https://github.com/IDEA-Research/Grounded-Segment-Anything

pip install -U huggingface_hub umap-learn git+https://github.com/openai/CLIP.git

We will also need two more libraries: OpenAI’s CLIP GitHub repo, enabling us to generate image features with the CLIP model, and the umap-learn library, which will let us apply a dimensionality reduction technique called Uniform Manifold Approximation and Projection (UMAP) to those features to visualize them in 2D:

Biggest missing feature for all these self hosted photo hosting is the lack of a real search. Being able to search for things like "beach at night" is a time saver instead of browsing through hundreds or thousands of photos. There are trained neural networks out there like https://github.com/openai/CLIP which are quite good.

In computer vision, this is known as zero-shot learning, or zero-shot prediction, because the goal is to generate predictions without explicitly being given any example predictions to learn from. With the advent of high quality multimodal models like CLIP and foundation models like Segment Anything, it is now possible to generate remarkably good zero-shot predictions for a variety of computer vision tasks, including:

(Github Repo | Most Popular Model | Paper | Project Page)

the first thing that comes to mind is CLIP: https://github.com/openai/CLIP

Whenever I’m working on semantic search applications that connect images and text, I start with a family of models known as contrastive language image pre-training (CLIP). These models are trained on image-text pairs to generate similar vector representations or embeddings for images and their captions, and dissimilar vectors when images are paired with other text strings. There are multiple CLIP-style models, including OpenCLIP and MetaCLIP, but for simplicity we’ll focus on the original CLIP model from OpenAI. No model is perfect, and at a fundamental level there is no right way to compare images and text, but CLIP certainly provides a good starting point.

in the modal card it says: pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).

Command: "C:\StabilityMatrix\Packages\stable-diffusion-webui\venv\Scripts\python.exe" -m pip install https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip --prefer-binary

CLIP might work for your needs.