Merge branch 'dev' of https://github.com/windows-toolkit/Labs-IntelligentAPIs into dev

README.md

@@ -1 +1,48 @@
-# ProjectBangalore
+<h1 align="center">
+  Intelligent APIs
+</h1>
+
+## What is IntelligentAPIs?
+
+Intelligent APIs aims to make machine learning tasks easier for developers to leverage in their applications without needing ML expertise or creating a new model.
+By just importing a nuget package and calling a function, we want developers to be able to build intelligent app experiences without needing to deal with the complexities of inferencing machine learning models on Windows.
+
+## Pre-requisites
+
+[Visual Studio 2017 Version 15.7.4 or Newer](https://developer.microsoft.com/en-us/windows/downloads/)
+
+[Windows 10 - Build 17763 (RS5) or Newer](https://www.microsoft.com/en-us/software-download/windowsinsiderpreviewiso)
+
+[Windows SDK - Build 17763 (RS5) or Newer](https://www.microsoft.com/en-us/software-download/windowsinsiderpreviewSDK)
+
+## What has been done so far?
+
+We have 2 nuget packages ready for you to test and play around with. These nuget packages enable you to perform classic machine learning tasks like image classification and object detection in 1-2 lines of code. 
+
+1. Image classification: To perform image classification, import the `IntelligentAPI_ImageClassifier` nuget package. To classify an image, you will need to pass a StorageFile object which is the image file itself, and the number of top results that you want. In the following example, we pass an image of a Rottweiler and we want the top 3 results.
+
+```C#
+   using IntelligentAPI.ImageClassification;  
+   ...
+   List<SqueezeNetResult> imageClasses = await SqueezeNetImageClassifier.ClassifyImage(selectedStorageFile, 3);
+```
+<div  align="center">
+<img src="https://user-images.githubusercontent.com/22471775/125314778-5a977780-e2eb-11eb-983f-0dde00b34e18.png" alt="drawing" width="400"/>
+</div>
+
+
+This nuget package performs [SqueezeNet](https://github.com/onnx/models/tree/master/vision/classification/squeezenet) model inferencing using [WinML](https://github.com/microsoft/Windows-Machine-Learning). SqueezeNet can detect [1000 different classes](https://github.com/onnx/models/blob/master/vision/classification/synset.txt).
+
+1. Object Detection: To perform object detection on your images/video, import the `IntelligentAPI_ObjectDetector` nuget package. To detect objects in the image, you can either pass an image file as a StorageFile object, or pass a VideoFrame.
+
+```C#
+   using IntelligentAPI.ObjectDetection;
+   ...
+   List<DetectionResult> listOfObjects = await YOLOObjectDetector.DetectObjects(selectedStorageFile);    
+```
+
+This nuget package performs object detection using [YOLOv4](https://github.com/hunglc007/tensorflow-yolov4-tflite) model inference on WinML and also return the co-ordinates of the bounding boxes around the detected objects. YOLOv4 can detect objects of [80 different classes](https://github.com/hunglc007/tensorflow-yolov4-tflite/blob/9f16748aa3f45ff240608da4bd9b1216a29127f5/android/app/src/main/assets/coco.txt).
+<div  align="center">
+<img src="https://user-images.githubusercontent.com/22471775/125318395-a861af00-e2ee-11eb-97b4-87b0fb4a5460.png" alt="drawing" width="300"/>
+</div>
+