Merge branch 'main' into atqy/refactor-fraud-detection

Author: atqy

introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-transfer-learning.ipynb

@@ -25,7 +25,7 @@
    "source": [
     "## Introduction\n",
     "\n",
-    "Welcome to our end-to-end example of distributed image classification algorithm in transfer learning mode. In this demo, we will use the Amazon sagemaker image classification algorithm in transfer learning mode to fine-tune a pre-trained model (trained on imagenet data) to learn to classify a new dataset. In particular, the pre-trained model will be fine-tuned using [caltech-256 dataset](http://www.vision.caltech.edu/Image_Datasets/Caltech256/). \n",
+    "Welcome to our end-to-end example of distributed image classification algorithm in transfer learning mode. In this demo, we will use the Amazon sagemaker image classification algorithm in transfer learning mode to fine-tune a pre-trained model (trained on imagenet data) to learn to classify a new dataset. In particular, the pre-trained model will be fine-tuned using [Caltech-256 dataset](https://paperswithcode.com/dataset/caltech-256). \n",
     "\n",
     "To get started, we need to set up the environment with a few prerequisite steps, for permissions, configurations, and so on."
    ]

sagemaker-clarify/computer_vision/image_classification/explainability_image_classification.ipynb

@@ -35,7 +35,7 @@
     "1. Explaining the importance of the image features (super pixels) for Image Classification model.\n",
     "1. Accessing the reports and output images.\n",
     "\n",
-    "In doing so, the notebook will first train and deploy an [Image Classification](https://github.com/aws/amazon-sagemaker-examples/blob/master/introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-transfer-learning-highlevel.ipynb) model with Sagemaker Estimator using [caltech-256 dataset](http://www.vision.caltech.edu/Image_Datasets/Caltech256/) [1], then use SageMaker Clarify to run explainability on a subset of test images.\n",
+    "In doing so, the notebook will first train and deploy an [Image Classification](https://github.com/aws/amazon-sagemaker-examples/blob/master/introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-transfer-learning-highlevel.ipynb) model with Sagemaker Estimator using [Caltech-256 dataset](https://paperswithcode.com/dataset/caltech-256) [1], then use SageMaker Clarify to run explainability on a subset of test images.\n",
     ">[1] Griffin, G. Holub, AD. Perona, P. The Caltech 256. Caltech Technical Report."
    ]
   },

sagemaker_neo_compilation_jobs/imageclassification_caltech/Image-classification-fulltraining-highlevel-neo.ipynb

@@ -21,7 +21,7 @@
     "\n",
     "***\n",
     "\n",
-    "Welcome to our model optimization example for image classification. In this demo, we will use the Amazon SageMaker Image Classification algorithm to train on the [caltech-256 dataset](http://www.vision.caltech.edu/Image_Datasets/Caltech256/) and then we will demonstrate Amazon SageMaker Neo's ability to optimize models."
+    "Welcome to our model optimization example for image classification. In this demo, we will use the Amazon SageMaker Image Classification algorithm to train on the [Caltech-256 dataset](https://paperswithcode.com/dataset/caltech-256) and then we will demonstrate Amazon SageMaker Neo's ability to optimize models."
    ]
   },
   {
@@ -121,7 +121,7 @@
    "source": [
     "### Data preparation\n",
     "\n",
-    "In this demo, we are using [Caltech-256](http://www.vision.caltech.edu/Image_Datasets/Caltech256/) dataset, pre-converted into `RecordIO` format using MXNet's [im2rec](https://mxnet.apache.org/versions/1.7/api/faq/recordio) tool. Caltech-256 dataset contains 30608 images of 256 objects. For the training and validation data, the splitting scheme followed is governed by this [MXNet example](https://github.com/apache/incubator-mxnet/blob/8ecdc49cf99ccec40b1e342db1ac6791aa97865d/example/image-classification/data/caltech256.sh). The example randomly selects 60 images per class for training, and uses the remaining data for validation. It takes around 50 seconds to convert the entire Caltech-256 dataset (~1.2GB) into `RecordIO` format on a p2.xlarge instance. SageMaker's training algorithm takes `RecordIO` files as input. For this demo, we will download the `RecordIO` files and upload it to S3. We then initialize the 256 object categories as well to a variable."
+    "In this demo, we are using Caltech-256 dataset, pre-converted into `RecordIO` format using MXNet's [im2rec](https://mxnet.apache.org/versions/1.7/api/faq/recordio) tool. Caltech-256 dataset contains 30608 images of 256 objects. For the training and validation data, the splitting scheme followed is governed by this [MXNet example](https://github.com/apache/incubator-mxnet/blob/8ecdc49cf99ccec40b1e342db1ac6791aa97865d/example/image-classification/data/caltech256.sh). The example randomly selects 60 images per class for training, and uses the remaining data for validation. It takes around 50 seconds to convert the entire Caltech-256 dataset (~1.2GB) into `RecordIO` format on a p2.xlarge instance. SageMaker's training algorithm takes `RecordIO` files as input. For this demo, we will download the `RecordIO` files and upload it to S3. We then initialize the 256 object categories as well to a variable."
    ]
   },
   {