ultralytics 8.0.99 HUB resume fix and Docs updates (#2567)

Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>

docs/models/sam.md

@@ -1,26 +1,37 @@
 ---
 comments: true
-description: Learn about the Vision Transformer (ViT) and segment anything with SAM models. Train and use pre-trained models with Python API.
+description: Learn about the Segment Anything Model (SAM) and how it provides promptable image segmentation through an advanced architecture and the SA-1B dataset.
 ---
 
-# Vision Transformers
+# Segment Anything Model (SAM)
 
-Vit models currently support Python environment:
+## Overview
+
+The Segment Anything Model (SAM) is a groundbreaking image segmentation model that enables promptable segmentation with real-time performance. It forms the foundation for the Segment Anything project, which introduces a new task, model, and dataset for image segmentation. SAM is designed to be promptable, allowing it to transfer zero-shot to new image distributions and tasks. The model is trained on the [SA-1B dataset](https://ai.facebook.com/datasets/segment-anything/), which contains over 1 billion masks on 11 million licensed and privacy-respecting images. SAM has demonstrated impressive zero-shot performance, often surpassing prior fully supervised results.
+
+![Dataset sample image](https://production-media.paperswithcode.com/datasets/540cfe0d-5fe7-43a4-9e1d-4c5813ddeb3e.png)
+
+## Key Features
+
+- **Promptable Segmentation Task:** SAM is designed for a promptable segmentation task, enabling it to return a valid segmentation mask given any segmentation prompt, such as spatial or text information identifying an object.
+- **Advanced Architecture:** SAM utilizes a powerful image encoder, a prompt encoder, and a lightweight mask decoder. This architecture enables flexible prompting, real-time mask computation, and ambiguity awareness in segmentation.
+- **SA-1B Dataset:** The Segment Anything project introduces the SA-1B dataset, which contains over 1 billion masks on 11 million images. This dataset is the largest segmentation dataset to date, providing SAM with a diverse and large-scale source of data for training.
+- **Zero-Shot Performance:** SAM demonstrates remarkable zero-shot performance across a range of segmentation tasks, allowing it to be used out-of-the-box with prompt engineering for various applications.
+
+For more information about the Segment Anything Model and the SA-1B dataset, please refer to the [Segment Anything website](https://segment-anything.com) and the research paper [Segment Anything](https://arxiv.org/abs/2304.02643).
+
+## Usage
+
+SAM can be used for a variety of downstream tasks involving object and image distributions beyond its training data. Examples include edge detection, object proposal generation, instance segmentation, and preliminary text-to-mask prediction. By employing prompt engineering, SAM can adapt to new tasks and data distributions in a zero-shot manner, making it a versatile and powerful tool for image segmentation tasks.
 
 ```python
 from ultralytics.vit import SAM
 
-# from ultralytics.vit import MODEL_TYPE
-
-model = SAM("sam_b.pt")
+model = SAM('sam_b.pt')
 model.info()  # display model information
-model.predict(...)  # predict
+model.predict('path/to/image.jpg')  # predict
 ```
 
-# Segment Anything
-
-## About
-
 ## Supported Tasks
 
 | Model Type | Pre-trained Weights | Tasks Supported       |
@@ -34,4 +45,21 @@ model.predict(...)  # predict
 |------------|--------------------|
 | Inference  | :heavy_check_mark: |
 | Validation | :x:                |
-| Training   | :x:                |
+| Training   | :x:                |
+
+# Citations and Acknowledgements
+
+If you use SAM in your research or development work, please cite the following paper:
+
+```bibtex
+@misc{kirillov2023segment,
+      title={Segment Anything}, 
+      author={Alexander Kirillov and Eric Mintun and Nikhila Ravi and Hanzi Mao and Chloe Rolland and Laura Gustafson and Tete Xiao and Spencer Whitehead and Alexander C. Berg and Wan-Yen Lo and Piotr Dollár and Ross Girshick},
+      year={2023},
+      eprint={2304.02643},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+
+We would like to acknowledge Meta AI for creating and maintaining this valuable resource for the computer vision community.