YOLO-World VS openvino_notebooks

2024 is shaping up to be the year of multimodal machine learning. From real-time text-to-image models and open-world vocabulary models to multimodal large language models like GPT-4V and Gemini Pro Vision, AI is primed for an unprecedented array of interactive multimodal applications and experiences.

Post author here. I like this idea. I plan to explore it and make a more generic solution. I'd love to have a point-and-click interface for annotating scenes.

For example, I'd like to be able to click on pieces of coffee equipment in a photo of my coffee setup so I can add sticky note annotations when you hover over each item.

For the bookshelves idea specifically, I would love to have a correction system in place. The problem isn't so much SAM as it is Grounding DINO, the model I'm using for object identification. I then pass each identified region to SAM and map the segmentation mask to the box.

Grounding DINO detects a lot of book spines, but often misses 1-2. I am planning to try out YOLO-World (https://github.com/AILab-CVC/YOLO-World), which, in my limited testing, performs better for this task.

Also, credits for using zram in your VM setup; that's a smart hack for memory management. Have you tried tweaking other models like the ones in this OpenVINO notebook?