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@@ -19,8 +19,6 @@ In this repo, we've supported the following demo with **simple implementations**
Grounded SAM 2 does not introduce significant methodological changes compared to [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://arxiv.org/abs/2401.14159). Both approaches leverage the capabilities of open-world models to address complex visual tasks. Consequently, we try to **simplify the code implementation** in this repository, aiming to enhance user convenience. Grounded SAM 2 does not introduce significant methodological changes compared to [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://arxiv.org/abs/2401.14159). Both approaches leverage the capabilities of open-world models to address complex visual tasks. Consequently, we try to **simplify the code implementation** in this repository, aiming to enhance user convenience.
[![Video Name](./assets/grounded_sam_2_intro.jpg)](https://github.com/user-attachments/assets/f0fb0022-779a-49fb-8f46-3a18a8b4e893)
## Latest updates ## Latest updates
- `2024/10/24`: Support [SAHI (Slicing Aided Hyper Inference)](https://docs.ultralytics.com/guides/sahi-tiled-inference/) on Grounded SAM 2 (with Grounding DINO 1.5) which may be helpful for inferencing high resolution image with dense small objects (e.g. **4K** images). - `2024/10/24`: Support [SAHI (Slicing Aided Hyper Inference)](https://docs.ultralytics.com/guides/sahi-tiled-inference/) on Grounded SAM 2 (with Grounding DINO 1.5) which may be helpful for inferencing high resolution image with dense small objects (e.g. **4K** images).