Merge branch 'master' of https://github.com/xzhou33/amazon-sagemaker-examples

Author: xzhou33

README.md

@@ -43,6 +43,7 @@ These examples provide quick walkthroughs to get you up and running with Amazon
 - [XGBoost for multi-class classification](introduction_to_amazon_algorithms/xgboost_mnist) uses Amazon SageMaker's implementation of [XGBoost](https://github.com/dmlc/xgboost) to classify handwritten digits from the MNIST dataset as one of the ten digits using a multi-class classifier. Both single machine and distributed use-cases are presented.
 - [DeepAR for time series forecasting](introduction_to_amazon_algorithms/deepar_synthetic) illustrates how to use the Amazon SageMaker DeepAR algorithm for time series forecasting on a synthetically generated data set.
 - [BlazingText Word2Vec](introduction_to_amazon_algorithms/blazingtext_word2vec_text8) generates Word2Vec embeddings from a cleaned text dump of Uncyclopedia articles using SageMaker's fast and scalable BlazingText implementation.
+- [Object Detection](introduction_to_amazon_algorithms/object_detection_pascalvoc_coco) illustrates how to train an object detector using the Amazon SageMaker Object Detection algorithm with different input formats (RecordIO and image).
 
 ### Scientific Details of Algorithms
 
@@ -111,4 +112,4 @@ These Amazon SageMaker examples fully illustrate a concept, but may require some
 
 *How do I contribute my own example notebook?*
 
-- Although we're extremely excited to receive contributions from the community, we're still working on the best mechanism to take in examples from external sources.  Please bear with us in the short-term if pull requests take longer than expected or are closed.
+- Although we're extremely excited to receive contributions from the community, we're still working on the best mechanism to take in examples from external sources.  Please bear with us in the short-term if pull requests take longer than expected or are closed.