Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions,
see Getting Started with Amazon Web Services in China
(PDF).
How Object Detection Works
The object detection algorithm identifies and locates all instances of objects in an
image from a known collection of object categories. The algorithm takes an image as
input and outputs the category that the object belongs to, along with a confidence score
that it belongs to the category. The algorithm also predicts the object's location and
scale with a rectangular bounding box. Amazon SageMaker Object Detection uses the Single Shot multibox Detector
(SSD) algorithm that takes a convolutional neural network (CNN) pretrained
for classification task as the base network. SSD uses the output of intermediate layers
as features for detection.
Various CNNs such as VGG and
ResNet have achieved great
performance on the image classification task. Object detection in Amazon SageMaker supports both
VGG-16 and ResNet-50 as a base network for SSD. The algorithm can be trained in full
training mode or in transfer learning mode. In full training mode, the base network is
initialized with random weights and then trained on user data. In transfer learning
mode, the base network weights are loaded from pretrained models.
The object detection algorithm uses standard data augmentation operations, such as
flip, rescale, and jitter, on the fly internally to help avoid overfitting.