--- layout: post comments: true title: Post Template author: UCLAdeepvision date: 2022-01-18 --- > This block is a brief introduction of your project. You can put your abstract here or any headers you want the readers to know. {: class="table-of-content"} * TOC {:toc} ## Main Content Your survey starts here. You can refer to the [source code](https://github.com/lilianweng/lil-log/tree/master/_posts) of [lil's blogs](https://lilianweng.github.io/lil-log/) for article structure ideas or Markdown syntax. We've provided a [sample post](https://ucladeepvision.github.io/CS188-Projects-2022Winter/2017/06/21/an-overview-of-deep-learning.html) from Lilian Weng and you can find the source code [here](https://raw.githubusercontent.com/UCLAdeepvision/CS188-Projects-2022Winter/main/_posts/2017-06-21-an-overview-of-deep-learning.md) ## Basic Syntax ### Image Please create a folder with the name of your team id under /assets/images/, put all your images into the folder and reference the images in your main content. You can add an image to your survey like this: ![YOLO]({{ '/assets/images/UCLAdeepvision/object_detection.png' | relative_url }}) {: style="width: 400px; max-width: 100%;"} *Fig 1. YOLO: An object detection method in computer vision* [1]. Please cite the image if it is taken from other people's work. ### Table Here is an example for creating tables, including alignment syntax. | | column 1 | column 2 | | :--- | :----: | ---: | | row1 | Text | Text | | row2 | Text | Text | ### Code Block ``` # This is a sample code block import torch print (torch.__version__) ``` ### Formula Please use latex to generate formulas, such as: $$ \tilde{\mathbf{z}}^{(t)}_i = \frac{\alpha \tilde{\mathbf{z}}^{(t-1)}_i + (1-\alpha) \mathbf{z}_i}{1-\alpha^t} $$ or you can write in-text formula $$y = wx + b$$. ### More Markdown Syntax You can find more Markdown syntax at [this page](https://www.markdownguide.org/basic-syntax/). ## Reference Please make sure to cite properly in your work, for example: [1] Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." *Proceedings of the IEEE conference on computer vision and pattern recognition*. 2016. ---