Does training a tflite model require images annotated?
The answer to your question is depends on what model do you plan to train.
In the case of a model for food detection and segmentation you do need annotations when training. If you do not provide the model with labeled training data as it is a supervised learning model it cannot learn from them.
If you were to train an autoencoder the data does not need to be annotated. Hope the keywords used in this answer help you out search for more information about the topic.
I am trying to implement TFLite model for food detection and segmentation. This is the model i chose suitable for my food images dataset: [https://tfhub.dev/s?deployment-format=lite&q=inception%20resnet%20v2].
I searched over google to understand how the images are required to be annotated, but only end up in confusion. I understand the dataset is converted to TFRecords and then fed to the pretrained model. But for training the model with custom dataset, does not it require an annotation file? I dont see any info about this on TF hub either.
Please can anyone help me on this!