Merge pull request #567 from microsoft/staging

Sync master <-> staging

Author: PatrickBue

README.md

@@ -1,10 +1,8 @@
 <img src="scenarios/media/logo_cvbp.png" align="right" alt="" width="300"/>
 
 ```diff
-+ March 27: Released v1.1 with new and improved
-+   functionality for image retrieval, object detection,
-+   keypoint detection and action recognition.  
-+   For additional details, please refer to our releases page.
++ Update June 24: Added action recognition as new core scenario. 
++                 Object tracking coming soon (in 2-4 weeks).
 ```
 
 # Computer Vision
@@ -56,10 +54,10 @@ The following is a summary of commonly used Computer Vision scenarios that are c
 | [Detection](scenarios/detection) | Base | Object Detection is a technique that allows you to detect the bounding box of an object within an image. |
 | [Keypoints](scenarios/keypoints) | Base | Keypoint detection can be used to detect specific points on an object. A pre-trained model is provided to detect body joints for human pose estimation. |
 | [Segmentation](scenarios/segmentation) | Base | Image Segmentation assigns a category to each pixel in an image. |
-| [Action recognition](contrib/action_recognition) | Contrib | Action recognition to identify in video/webcam footage what actions are performed (e.g. "running", "opening a bottle") and at what respective start/end times.|
+| [Action recognition](scenarios/action_recognition) | Base | Action recognition to identify in video/webcam footage what actions are performed (e.g. "running", "opening a bottle") and at what respective start/end times. We also implemented the i3d implementation of action recognition that can be found under (contrib)[contrib]. |
 | [Crowd counting](contrib/crowd_counting) | Contrib | Counting the number of people in low-crowd-density (e.g. less than 10 people) and high-crowd-density (e.g. thousands of people) scenarios.|
 
-We separate the supported CV scenarios into two locations: (i) **base**: code and notebooks within the "utils_cs" and "scenarios" folders which follow strict coding guidelines, are well tested and maintained; (ii) **contrib**: code and other assets within the "contrib" folder, mainly covering less common CV scenarios using bleeding edge state-of-the-art approaches. Code in "contrib" is not regularly tested or maintained.
+We separate the supported CV scenarios into two locations: (i) **base**: code and notebooks within the "utils_cv" and "scenarios" folders which follow strict coding guidelines, are well tested and maintained; (ii) **contrib**: code and other assets within the "contrib" folder, mainly covering less common CV scenarios using bleeding edge state-of-the-art approaches. Code in "contrib" is not regularly tested or maintained.
 
 ## Computer Vision on Azure
 

contrib/README.md

@@ -8,11 +8,12 @@ Each project should live in its own subdirectory ```/contrib/<project>``` and co
 ## Scenarios
 | Directory | Project description | Build status (optional) |
 |---|---|---|
-| [Action recognition](action_recognition) | Action recognition to identify in video/webcam footage what actions are performed (e.g. "running", "opening a bottle") and at what respective start/end times.| |
 | [Crowd counting](crowd_counting) | Counting the number of people in low-crowd-density (e.g. less than 10 people) and high-crowd-density (e.g. thousands of people) scenarios. | [![Build Status](https://dev.azure.com/team-sharat/crowd-counting/_apis/build/status/lixzhang.cnt?branchName=lixzhang%2Fsubmodule-rev3)](https://dev.azure.com/team-sharat/crowd-counting/_build/latest?definitionId=49&branchName=lixzhang%2Fsubmodule-rev3)|
+| [Action Recognition with I3D](action_recognition) | Action recognition to identify video/webcam footage from what actions are performed (e.g. "running", "opening a bottle") and at what respective start/end times. Please note, that we also have a R(2+1)D implementation of action recognition that you can find under [scenarios](../sceanrios).| |
 
 ## Tools
 | Directory | Project description | Build status (optional) |
 |---|---|---|
+| [HTML Demo](html_demo) | These files provide an HTML web page that allows users to visualize the output of a deployed computer vision DNN model. Users can improve on and gain insights from their deployed model by uploading query/test images and examining the model results for correctness through the user interface. The interface includes sample query/test images for testing your own model and example output for 3 types of models: image classification, object detection, and image similarity. | |
 | [vm_builder](vm_builder) | This script helps users create a single Ubuntu Data Science Virtual Machine with a GPU with the computer vision recipes repo installed and ready to be used. If you find the script to be out-dated or not working, you can create the VM using the Azure portal or the Azure CLI tool with a few more steps. | |
 | [vmss_builder](vmss_builder) | This script helps you setup a cluster of virtual machines with the computer vision recipes repo pre-installed using VMSS. This cluster is designed to be temporal, ie to be spun up and torn down. Users for this cluster will be prescribed a username/password/ip. This setup can be used for hands-on / lab sessions when you need to prepare multiple VM environments for a short period.|

contrib/action_recognition/README.md

@@ -1,17 +1,12 @@
 # Action Recognition
 
-```diff
-+ Feb 2020: We are working on moving code from this folder to scenarios\action_recognition.
-+           While this work is ongoing, please visit both locations for implementations and documentation.
-```
-
 This directory contains resources for building video-based action recognition systems.
 
 Action recognition (also known as activity recognition) consists of classifying various actions from a sequence of frames:
 
 ![](./media/action_recognition2.gif "Example of action recognition")
 
-We implemented two state-of-the-art approaches: (i) [I3D](https://arxiv.org/pdf/1705.07750.pdf) and (ii) [R(2+1)D](https://arxiv.org/abs/1711.11248). This includes example notebooks for e.g. scoring of webcam footage or fine-tuning on the [HMDB-51](http://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/) dataset.
+We implemented two state-of-the-art approaches: (i) [I3D](https://arxiv.org/pdf/1705.07750.pdf) and (ii) [R(2+1)D](https://arxiv.org/abs/1711.11248). This includes example notebooks for e.g. scoring of webcam footage or fine-tuning on the [HMDB-51](http://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/) dataset. The latter can be accessed under [scenarios](../scenarios) at the root level.
 
 We recommend to use the **R(2+1)D** model for its competitive accuracy, fast inference speed, and less dependencies on other packages. For both approaches, using our implementations, we were able to reproduce reported accuracies:
 
@@ -27,5 +22,5 @@ We recommend to use the **R(2+1)D** model for its competitive accuracy, fast inf
 
 | Directory |  Description |
 | -------- |  ----------- |
-| [r2p1d](r2p1d)  | Scripts for fine-tuning a pre-trained R(2+1)D model on HMDB-51 dataset
-| [i3d](i3d)  | Scripts for fine-tuning a pre-trained I3D model on HMDB-51 dataset
+| [i3d](i3d) | Scripts for fine-tuning a pre-trained I3D model on HMDB-51
+dataset. |