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Remove image "?" args (#15891)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-08-29 14:11:59 +02:00 • committed by GitHub
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docs/en/models/mobile-sam.md
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@ -4,7 +4,7 @@ description: Discover MobileSAM, a lightweight and fast image segmentation model
keywords: MobileSAM, image segmentation, lightweight model, fast segmentation, mobile applications, SAM, ViT encoder, Tiny-ViT, Ultralytics
---
![MobileSAM Logo](https://github.com/ChaoningZhang/MobileSAM/blob/master/assets/logo2.png?raw=true)
![MobileSAM Logo](https://github.com/ChaoningZhang/MobileSAM/blob/master/assets/logo2.png)
# Mobile Segment Anything (MobileSAM)
@ -53,9 +53,9 @@ Here is the comparison of the whole pipeline:
The performance of MobileSAM and the original SAM are demonstrated using both a point and a box as prompts.
![Image with Point as Prompt](https://raw.githubusercontent.com/ChaoningZhang/MobileSAM/master/assets/mask_box.jpg?raw=true)
![Image with Point as Prompt](https://raw.githubusercontent.com/ChaoningZhang/MobileSAM/master/assets/mask_box.jpg)
![Image with Box as Prompt](https://raw.githubusercontent.com/ChaoningZhang/MobileSAM/master/assets/mask_box.jpg?raw=true)
![Image with Box as Prompt](https://raw.githubusercontent.com/ChaoningZhang/MobileSAM/master/assets/mask_box.jpg)
With its superior performance, MobileSAM is approximately 5 times smaller and 7 times faster than the current FastSAM. More details are available at the [MobileSAM project page](https://github.com/ChaoningZhang/MobileSAM).

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docs/en/models/sam-2.md
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@ -8,7 +8,7 @@ keywords: SAM 2, Segment Anything, video segmentation, image segmentation, promp
SAM 2, the successor to Meta's [Segment Anything Model (SAM)](sam.md), is a cutting-edge tool designed for comprehensive object segmentation in both images and videos. It excels in handling complex visual data through a unified, promptable model architecture that supports real-time processing and zero-shot generalization.
![SAM 2 Example Results](https://github.com/facebookresearch/segment-anything-2/raw/main/assets/sa_v_dataset.jpg?raw=true)
![SAM 2 Example Results](https://github.com/facebookresearch/segment-anything-2/raw/main/assets/sa_v_dataset.jpg)
## Key Features
@ -54,7 +54,7 @@ SAM 2 sets a new benchmark in the field, outperforming previous models on variou
- **Memory Mechanism**: Includes a memory encoder, memory bank, and memory attention module. These components collectively store and utilize information from past frames, enabling the model to maintain consistent object tracking over time.
- **Mask Decoder**: Generates the final segmentation masks based on the encoded image features and prompts. In video, it also uses memory context to ensure accurate tracking across frames.
![SAM 2 Architecture Diagram](https://github.com/facebookresearch/segment-anything-2/blob/main/assets/model_diagram.png?raw=true)
![SAM 2 Architecture Diagram](https://github.com/facebookresearch/segment-anything-2/blob/main/assets/model_diagram.png)
### Memory Mechanism and Occlusion Handling