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Chapter 13
We will discuss AutoML in Chapter 14, An Introduction to AutoML, and the interested
reader can refer to MNAS documentation in the references [4] for applications to
mobile.
Object detection
TensorFlow Lite comes with a pretrained model that can detect multiple objects
within an image, with bounding boxes. 80 different classes of objects are recognized.
The network is based on a pretrained quantized COCO SSD MobileNet v1 model.
For each object, the model provides the class, the confidence of detection, and the
vertices of the bounding boxes (https://www.tensorflow.org/lite/models/
object_detection/overview).
Pose estimation
TensorFlow Lite includes a pretrained model for detecting parts of human bodies
in an image or a video. For instance, it is possible to detect noses, left/right eyes,
hips, ankles, and many other parts. Each detection comes with an associated
confidence score (https://www.tensorflow.org/lite/models/pose_estimation/
overview).
Smart reply
TensorFlow Lite has also a pretrained model for generating replies to chat
messages. These replies are contextualized and similar to what is available on
Gmail (https://www.tensorflow.org/lite/models/smart_reply/overview).
Segmentation
TensorFlow Lite has also a pretrained model (https://www.tensorflow.org/
lite/models/segmentation/overview) for image segmentation, where the goal is
to decide what the semantic labels (for example, person, dog, cat) assigned to every
pixel in the input image are. Segmentation is based on the DeepLab algorithm [5].
Style transfer
TensorFlow Lite supports artistic style transfer (see Chapter 5, Advanced
Convolutional Neural Networks) via a combination of a MobileNetV2-based neural
network, which reduces the input style image to a 100-dimension style vector, and a
style transform model, which applies the style vector to a content image to create the
stylized image (https://www.tensorflow.org/lite/models/style_transfer/
overview).