 Benchmark Datasets
#
# - Image classification is a supervised task.
# - The number of parameters to train a CNN might be huge, thus required a lot of labeled images and huge datasets are required.
# - Luckily research groups across the globe have been working toward collecting, hand annotating, and crowdsourcing different datasets.
# - These datasets are utilized to benchmark performance of different algorithms.
# - Below is a short list of widely accepted benchmarking datasets in the field of image classification:
#     - **ImageNet:** 14M images, 20K categories, Princeton University, 2009
#     - **80 Millions Tiny Images datasets:** 80M low resolution images colled from the internet, 75K non-abstract nouns, MIT. It is the basis for other famous datasets including CIFAR
#     - **CIFAR-10:** 60K low resolution images, 10 non-overlapping classes, Canadian Institute for Advanced Research. Most wide used dataset in ML
#     - **CIFAR-100:** 60K low resolution images evenly spread across 100 classes, Canadian Institute for Advanced Research
#     - **Common Object in Context (COCO):** 200K images evenly spread across different classes. It is a large scale visual database for object identification, segmentation, and captioning
#     - **Open Images:** 9M images with labels
#     - **Caltech 101 and Caltech 256:** 9K and 30K images respectively, spanning across 101 and 256 classes
#     - **Stanford Dog dataset: 20K colord images, 200 classes.** It is specific to different dog breads
#     - **MNIST:** 60K hand-labeled digits (zero to nine). One of the most famous datasets for ML

# ### 1.4 State of the Art (SOTA) Deep Image Classification Models
#
# - Deep learning has received much attention and hype over the years, it is not a surprised that a lot of conferences, journals and competitions worldwide centered around that.
# - Image classification architectures have experienced iterative improvements on a regular basis.
# - Let's look at the best SOTA models:
#     - AlexNet
#     - VGG-16
#     - Inception (GoogleNet)
#     - ResNet
#     - MobileNet
#     - DenseNet
#     
# see lecture 5 for more information about SOTAs

# ## Part 2: Use Cases
#
# Let's use Transfer Learning, to build some applications. It is convenient to run the applications on Google Colab. Check out the links below. 
#
# ### 2.1 Image Classification and Transfer Learning
#
# - You know what image classification is all about. Now we will use pretrained model(s) to understand how we can leverage transfer learning to improve upon our models.
# - In particular we are going to focus on fine-grained image classification which refers to the task of recognizing different sublcasses within a higher-level class.
# - We will focus around the Stanford Dogs Dataset which contains the images of different dog breeds. Hence the task is to categorize different dog breeds.
# - We are going to train the model on a **subset** of the entire dataset.
# - The keys areas of focus in this use case will be:
#     - Data preparation
#     - Train dog breeds classifier using Transfer Learning
# - Find more on the colab notebook [Lecture 6: Transfer Learning across Tasks: Image Classification and Transfer Learning](https://colab.research.google.com/drive/1vY5OSsebe5pRKicq9Db1ZdQ2pIHCfbsw)

# ### 2.2 Style Transfer
#  
# - Painting present a complex interplay of content and style. Photograph are a combination of perspectives and lights. Let's combine them for spectacular and surprising results.
# - Our goal is to modify the original image (a bird's eye view of the city of Venice) adding the style, colors and stroke paterns from a piece of art (in our case a mosaic designed by Gaudi')
# - The outcome will be the result of a Transfer Learning algorithm presented in the paper [A Neural Algorithm of Artistic Style
# ](https://arxiv.org/pdf/1508.06576.pdf)
# - The key areas of focus in this use case will be:
#     - Understanding neural style transfer
#     - Image preprocessing
#     - Building loss function
#     - Constructing a custom optimizer
#     - Style transfer in action
# - Find more on the colab notebook [Lecture 6:  Transfer Learning across Tasks: Style Transfer Model](https://colab.research.google.com/drive/19awyAD09rbdknV_jkfS4yMRFKW0223Ll)


<issue_start><issue_comment>Title: Fluent UI E2E tests randomly fail
username_0: ### Please provide a reproduction of the bug in a codepen:
Cypress E2E randomly fail. A problem was observed only in Dialog tests.

<img width="860" alt="Screenshot 2021-03-18 at 13 08 53" src="https://user-images.githubusercontent.com/77059398/111624071-80a08580-87eb-11eb-8939-baf4f685d2a0.png">

The problem occurs randomly in the pipeline or during locally run tests.

#### Actual behavior:

E2E tests are randomly failing.

#### Expected behavior:

E2E tests doesn't fail + Dialog tests are enabled again
<issue_comment>username_1: @username_0 - Thanks for reaching out and your suggestions posted. If you have an idea how to remedy this with a PR - we would love to see it.

@khmakoto - Would you be able to verify why the tests are failing for Dialog Comp?Military.
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