Merge pull request aws#577 from markproy/roymark_object_detection_2

Proposing a new sample Object Detection notebook using a public Birds dataset

Author: nadiaya

README.md

@@ -54,22 +54,23 @@ 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 Wikipedia 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).
+- [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). It uses the Pascal VOC dataset. A third notebook is provided to demonstrate the use of incremental training.
+- [Object detection for bird images](introduction_to_amazon_algorithms/object_detection_birds) demonstrates how to use the Amazon SageMaker Object Detection algorithm with a public dataset of Bird images.
 - [Object2Vec for movie recommendation](introduction_to_amazon_algorithms/object2vec_movie_recommendation) demonstrates how Object2Vec can be used to model data consisting of pairs of singleton tokens using movie recommendation as a running example.
 - [Object2Vec for multi-label classification](introduction_to_amazon_algorithms/object2vec_multilabel_genre_classification) shows how ObjectToVec algorithm can train on data consisting of pairs of sequences and singleton tokens using the setting of genre prediction of movies based on their plot descriptions.
 - [Object2Vec for sentence similarity](introduction_to_amazon_algorithms/object2vec_sentence_similarity) explains how to train Object2Vec using sequence pairs as input using sentence similarity analysis as the application.
 - [IP Insights for suspicious logins](introduction_to_amazon_algorithms/ipinsights_login) shows how to train IP Insights on a login events for a web server to identify suspicious login attempts.
 - [Semantic Segmentation](introduction_to_amazon_algorithms/semantic_segmentation_pascalvoc) shows how to train a semantic segmentation algorithm using the Amazon SageMaker Semantic Segmentation algorithm. It also demonstrates how to host the model and produce segmentaion masks and probability of segmentation.
 
-### Amazon SageMaker RL 
+### Amazon SageMaker RL
 
 The following provide examples demonstrating different capabilities of Amazon SageMaker RL.
 
 - [Cartpole using Coach](reinforcement_learning/rl_cartpole_coach) demonstrates the simplest usecase of Amazon SageMaker RL using Intel's RL Coach.
 - [AWS DeepRacer](reinforcement_learning/rl_deepracer_robomaker_coach_gazebo) demonstrates AWS DeepRacer trainig using RL Coach in the Gazebo environment.
 - [HVAC using EnergyPlus](reinforcement_learning/rl_hvac_coach_energyplus) demonstrates the training of HVAC systems using the EnergyPlus environment.
-- [Knapsack Problem](reinforcement_learning/rl_knapsack_coach_custom) demonstrates how to solve the knapsack problem using a custom environment. 
-- [Mountain Car](reinforcement_learning/rl_mountain_car_coach_gymEnv) Mountain car is a classic RL problem. This notebook explains how to solve this using the OpenAI Gym environment. 
+- [Knapsack Problem](reinforcement_learning/rl_knapsack_coach_custom) demonstrates how to solve the knapsack problem using a custom environment.
+- [Mountain Car](reinforcement_learning/rl_mountain_car_coach_gymEnv) Mountain car is a classic RL problem. This notebook explains how to solve this using the OpenAI Gym environment.
 - [Distributed Neural Network Compression](reinforcement_learning/rl_network_compression_ray_custom) This notebook explains how to compress ResNets using RL, using a custom environment and the RLLib toolkit.
 - [Turtlebot Tracker](reinforcement_learning/rl_objecttracker_robomaker_coach_gazebo) This notebook demonstrates object tracking using AWS Robomaker and RL Coach in the Gazebo environment.
 - [Portfolio Management](reinforcement_learning/rl_portfolio_management_coach_customEnv) This notebook uses a custom Gym environment to manage multiple financial investments.

introduction_to_amazon_algorithms/README.md

@@ -16,9 +16,10 @@ These examples provide quick walkthroughs to get you up and running with Amazon
 - [XGBoost for multi-class classification](xgboost_mnist) uses Amazon SageMaker's implementation of [XGBoost](https://github.com/dmlc/xgboost) to classifiy 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](deepar_synthetic) illustrates how to use the Amazon SageMaker DeepAR algorithm for time series forecasting on a synthetically generated data set.
 - [BlazingText Word2Vec](blazingtext_word2vec_text8) generates Word2Vec embeddings from a cleaned text dump of Wikipedia articles using SageMaker's fast and scalable BlazingText implementation.
+- [Object detection for bird images](object_detection_birds) demonstrates how to use the Amazon SageMaker Object Detection algorithm with a public dataset of Bird images.
+- [Object detection for Pascal VOC](object_detection_pascalvoc_coco) provides three sample notebooks that demonstrate how to use the Amazon SageMaker Object Detection algorithm with the Pascal VOC dataset. One uses the RecordIO format, and another uses JSON format. The third notebook shows how to use incremental training.
 - [Object2Vec for movie recommendation](object2vec_movie_recommendation) demonstrates how Object2Vec can be used to model data consisting of pairs of singleton tokens using movie recommendation as a running example.
 - [Object2Vec for multi-label classification](object2vec_multilabel_genre_classification) shows how ObjectToVec algorithm can train on data consisting of pairs of sequences and singleton tokens using the setting of genre prediction of movies based on their plot descriptions.
 - [Object2Vec for sentence similarity](object2vec_sentence_similarity) explains how to train Object2Vec using sequence pairs as input using sentence similarity analysis as the application.
 - [IP Insights for suspicious logins](ipinsights_login) shows how to train IP Insights on login events for a web server to identify suspicious login attempts.
 - [Semantic Segmentation](semantic_segmentation_pascalvoc) shows how to train a semantic segmentation algorithm using the Amazon SageMaker Semantic Segmentation algorithm. It also demonstrates how to host the model and produce segmentaion masks and probability of segmentation.
-