# [MLS-C01] [Algorithms] Image Analysis Algorithms

Posted by Oscaner on July 19, 2022

## Defined

• Supervised learning algorithms
• Take images as input and either labels the images or identifies objects in the image
• Two image analysis algorithms in the SageMaker built-in algorithms

## Use Cases

1. Facial recognition
2. Airport baggage scanning
3. Analyze social media images for missing persons
4. Real-time vehicle damage assessment
5. Medical image analysis
6. Building entrance security
7. Product line analysis

## SageMaker Algorithms

### Image Classification

• Supervised learning algorithm that supports multi-label classification
• Takes an image as input and outputs one or more labels assigned to that image
• Uses a convolutional neural network (ResNet) that can be trained from scratch or trained using transfer learning when a large number of training images are not available
• Can also seed the training of a new model with the artifacts from a model that you trained previously, called incremental training
• Recommended input format is RecordIO, but can also use raw images in .jpg or .png format.
• Example use case: classify image of person as on building entry security list
• Important Hyperparameters
1. num_classes: number of output classes
2. num_training_samples: number of training examples in the input dataset
3. early_stopping: define a threshold at which to stop training

### Object Detection

• Supervised learning algorithm
• Detects and classifies objects in images using a deep neural network
• Takes images as input and identifies all instances of objects within the image
• Object is categorized into one of the classes in a collection you specify, with a confidence score assigned to the class
• Location and scale of the object in the image are noted by bounding box
• Can be trained from scratch, or trained with models that have been pre-trained on the ImageNet dataset
• Use input format of RecordIO, but can also use raw images in .jpg or .png format.
• Example use case: detect objects in baggage at airport scan
• Important Hyperparameters
1. num_classes: number of output classes
2. num_training_samples: number of training examples in the input dataset
3. use_pretrained_model: use a pre-trained model for training