MASA: Matching Anything By Segmenting Anything (CVPR24) about yolo_tracking HOT 4 OPEN

Wow, this looks very promising

from yolo_tracking.

From MASA:

"Additionally, our learned detection head speeds up the original SAM dense uniform point proposals for segmenting everything by over tenfold, crucial for tracking applications."

"We treat the SAM outputs as dense object region proposals and learn to match those regions from a vast image collection. We further design a universal MAS encoder: A heavy ViT-based backbone for feature extraction."

From SAM:

"Given a precomputed image embedding, the prompt encoder and mask decoder run in a web browser, on CPU, in ∼50ms."

SAM uses ViT as image embedder, from ViT:

"ViT-B (Base Model): Inference times on high-end GPUs (such as NVIDIA V100 or A100) are typically around 20-40 milliseconds per image."

ViT-L (Large Model): Inference times are generally longer, around 50-80 milliseconds per image, depending on the exact setup and image resolution.

ViT-H (Huge Model): Inference times can exceed 100 milliseconds per image due to the increased model complexity and size.

This would require to get ViT working here as embedder to start with

from yolo_tracking.

This:

This would require to get ViT working here as embedder to start with

may not be a problem anymore 😄, given that Ultralytics has a ViT encoder

from yolo_tracking.

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Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

from yolo_tracking.