How Object Detection - TensorFlow Works

The Object Detection - TensorFlow algorithm takes an image as input and predicts bounding boxes and object labels. Various deep learning networks such as MobileNet, ResNet, Inception, and EfficientNet are highly accurate for object detection. There are also deep learning networks that are trained on large image datasets, such as Common Objects in Context (COCO), which has 328,000 images. After a network is trained with COCO data, you can then fine-tune the network on a dataset with a particular focus to perform more specific object detection tasks. The Amazon SageMaker Object Detection - TensorFlow algorithm supports transfer learning on many pretrained models that are available in the TensorFlow Model Garden.

According to the number of class labels in your training data, an object detection layer is attached to the pretrained TensorFlow model of your choice. You can then fine-tune either the entire network (including the pretrained model) or only the top classification layer on new training data. With this method of transfer learning, training with smaller datasets is possible.