Inception v3 was introduced by Google and achieved an error rate of 3.46%. You'll see that Inception v3 is significantly more complex. It also takes more resources to train this model, but the upside here is that we don't have to train it to use it.
We'll look into what we'll need to start using Inception v3 in our code. The model consists of layers and weight values present in classify_image_graph_def.pb.
We also have a list of labels, which the model can predict in imagenet_2012_challenge_label_map_proto.pbtxtfile and a document that allows the mapping of results of the neural network to the labels inimagenet_synset_to_human_label_map.txtfile.
Here's an example of the Panda image. First, we receive the IDs that score. The ...