wip

Author: EC2 Default User

README.md

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 ## Host Multiple TensorFlow computer vision models using Amazon SageMaker Multi-model endpoint
 
-CIFAR-10  is an established computer-vision dataset used for object recognition. It is a subset of the 80 million tiny images dataset and consists of 60,000 32x32 color images containing one of 10 object classes, with 6000 images per class.
+Amazon SageMaker Multi-Model Endpoints provides a scalable and cost-effective way to deploy large numbers of custom machine learning models. SageMaker Multi-Model endpoints will let you deploy multiple ML models on a single endpoint and serve them using a single serving container. Your application simply needs to include an API call with the target model to this endpoint to achieve low latency, high throughput inference. Instead of paying for a separate endpoint for every single model, you can host many models for the price of a single endpoint. For detailed information about multi-model endpoints, see Save on inference costs by using Amazon SageMaker multi-model endpoints.
+
+In this repository, we demonstrate how to host two computer vision models trained using the TensorFlow framework under one SageMaker multi-model endpoint.  For the first model, we train a smaller version of AlexNet CNN to classify images from the CIFAR-10 dataset. For the second model, we use a pretrained VGG16 CNN model pretrained on the ImageNet dataset and fine-tuned on the Sign Language Digits Dataset to classify hand symbol images. 
+
+### Model-1: CIFAR-10 image classification
+CIFAR-10 is a benchmark dataset for image classification in the CV and ML literature. CIFAR images are colored (three channels) with dramatic variation in how the objects appear. It consists of 32×32 color images in 10 classes, with 6,000 images per class. There are 50,000 training images and 10,000 test images. Figure below shows a sample of the images grouped by the labels.
+
+### Model-2: Sign language digits classification 
+For model-2, we will use the sign language digits dataset. This dataset distinguishes the sign language digits from 0 to 9. The figure below shows a sample of the dataset. 
+Following are the details of the dataset: 
+•	Number of classes = 10 (digits 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9) 
+•	Image size = 100 × 100
+•	Color space = RGB
+•	1,712 images in the training set 
+•	300 images in the validation set
+•	50 images in the test set 
+
 
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