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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 6 months ago committed by fcakyon
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  1. 2
      docs/en/guides/coral-edge-tpu-on-raspberry-pi.md
  2. 2
      docs/en/guides/preprocessing_annotated_data.md
  3. 2
      docs/en/integrations/ibm-watsonx.md
  4. 2
      docs/en/integrations/paddlepaddle.md
  5. 6
      docs/en/models/mobile-sam.md
  6. 4
      docs/en/models/sam-2.md

2
docs/en/guides/coral-edge-tpu-on-raspberry-pi.md
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@ -7,7 +7,7 @@ keywords: Coral Edge TPU, Raspberry Pi, YOLOv8, Ultralytics, TensorFlow Lite, ML
# Coral Edge TPU on a Raspberry Pi with Ultralytics YOLOv8 🚀
<p align="center">
<img width="800" src="https://images.ctfassets.net/2lpsze4g694w/5XK2dV0w55U0TefijPli1H/bf0d119d77faef9a5d2cc0dad2aa4b42/Edge-TPU-USB-Accelerator-and-Pi.jpg?w=800" alt="Raspberry Pi single board computer with USB Edge TPU accelerator">
<img width="800" src="https://images.ctfassets.net/2lpsze4g694w/5XK2dV0w55U0TefijPli1H/bf0d119d77faef9a5d2cc0dad2aa4b42/Edge-TPU-USB-Accelerator-and-Pi.jpg" alt="Raspberry Pi single board computer with USB Edge TPU accelerator">
</p>
## What is a Coral Edge TPU?

2
docs/en/guides/preprocessing_annotated_data.md
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@ -73,7 +73,7 @@ Here are some other benefits of data augmentation:
Common augmentation techniques include flipping, rotation, scaling, and color adjustments. Several libraries, such as Albumentations, Imgaug, and TensorFlow's ImageDataGenerator, can generate these augmentations.
<p align="center">
<img width="100%" src="https://i0.wp.com/ubiai.tools/wp-content/uploads/2023/11/UKwFg.jpg?fit=2204%2C775&ssl=1" alt="Overview of Data Augmentations">
<img width="100%" src="https://i0.wp.com/ubiai.tools/wp-content/uploads/2023/11/UKwFg.jpg" alt="Overview of Data Augmentations">
</p>
With respect to YOLOv8, you can [augment your custom dataset](../modes/train.md) by modifying the dataset configuration file, a .yaml file. In this file, you can add an augmentation section with parameters that specify how you want to augment your data.

2
docs/en/integrations/ibm-watsonx.md
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@ -15,7 +15,7 @@ You can train [Ultralytics YOLOv8 models](https://github.com/ultralytics/ultraly
[Watsonx](https://www.ibm.com/watsonx) is IBM's cloud-based platform designed for commercial generative AI and scientific data. IBM Watsonx's three components - watsonx.ai, watsonx.data, and watsonx.governance - come together to create an end-to-end, trustworthy AI platform that can accelerate AI projects aimed at solving business problems. It provides powerful tools for building, training, and [deploying machine learning models](../guides/model-deployment-options.md) and makes it easy to connect with various data sources.
<p align="center">
<img width="800" src="https://cdn.stackoverflow.co/images/jo7n4k8s/production/48b67e6aec41f89031a3426cbd1f78322e6776cb-8800x4950.jpg?auto=format" alt="Overview of IBM Watsonx">
<img width="800" src="https://cdn.stackoverflow.co/images/jo7n4k8s/production/48b67e6aec41f89031a3426cbd1f78322e6776cb-8800x4950.jpg" alt="Overview of IBM Watsonx">
</p>
Its user-friendly interface and collaborative capabilities streamline the development process and help with efficient model management and deployment. Whether for computer vision, predictive analytics, natural language processing, or other AI applications, IBM Watsonx provides the tools and support needed to drive innovation.

2
docs/en/integrations/paddlepaddle.md
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@ -13,7 +13,7 @@ The ability to export to PaddlePaddle model format allows you to optimize your [
## Why should you export to PaddlePaddle?
<p align="center">
<img width="75%" src="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/imgs/logo.png?raw=true" alt="PaddlePaddle Logo">
<img width="75%" src="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/imgs/logo.png" alt="PaddlePaddle Logo">
</p>
Developed by Baidu, [PaddlePaddle](https://www.paddlepaddle.org.cn/en) (**PA**rallel **D**istributed **D**eep **LE**arning) is China's first open-source deep learning platform. Unlike some frameworks built mainly for research, PaddlePaddle prioritizes ease of use and smooth integration across industries.

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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).

4
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

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