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I'm an EE who in his free time tinkers with Python and in the recent time trying to learn about neural networks and NCS. At the time, I'm working on a project that tries to guess person's age and gender and this is what I got so far:
I'm using this project project as a backbone. I'm using author's pretrained network as it does exactly what I need. My end goal is to make it run on NCS. How can I do it?
I can see that project is based on David Sandberg's Facenet and there is great example on ncappzoo that runs Facenet on NCS flawlessly. I thought it will be easy to convert the network I'm using the same way the example explains. I manage to compile my graph and load it to the NCS, just like ncappzoo does. But I can't achieve what the original project does - to read out age and gender estimation. The
output, userobj = graph.GetResult() returns an np array that, I assume, is 128 points of facial landmarks. The original project does something else:
gender = tf.argmax(tf.nn.softmax(gender_logits), 1) age_ = tf.cast(tf.constant([i for i in range(0, 101)]), tf.float32) age = tf.reduce_sum(tf.multiply(tf.nn.softmax(age_logits), age_), axis=1)