<p>In the research paper <a href="https://arxiv.org/pdf/1704.04861.pdf" rel="nofollow noreferrer">MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications</a>, the test accuracy using the 'MobileNet_1.0_224' architecture on the Stanford Dogs dataset is 83.3%, which seems in line with your results.</p>

<p>When you visually examine the Stanford Dogs Dataset you will find a lot of the breeds look similar, which makes it hard to reach a higher accuracy, even with the state of the art image classifiers in accuracy. You might improve your results by either splitting similar looking breeds into larger subcategories.</p>

<p>Alternatively, you might tweak the training settings of the retrain.py script in the Tensorflow for Poets tutorial, but the gains will be likely be marginal.</p>
Low validation accuracy after mobilenet transfer learning
<p>I need a tensorflow model which recognizes a dog's breed. I downloaded the Stanford Dogs Dataset - 20,580 images in 120 categories (=breeds). I followed the procedure described in <em>TensorFlow For Poets</em> to retrain mobilenet_1.0_224. I used <em>--how_many_training_steps=4000</em> and defaults for everything else. I got this tensorboard graph:
<a href="https://i.stack.imgur.com/ZZmE6.png" rel="nofollow noreferrer">Training and validation accuracy</a></p>

<p>The validation accuracy is only about 80%. </p>

<p>What can I do to improve it? </p>