<p>There are a few subtle differences. </p>

<ol>
<li><p>You are trying to apply ImageNet style architecture to Cifar-10. First convolution is <code>3 x 3</code>, not <code>7 x 7</code>. There is no max-pooling layer. The image is downsampled purely by using stride-2 convolutions.</p></li>
<li><p>You should probably do mean-centering by keeping <code>featurewise_center = True</code> in <code>ImageDataGenerator</code>.</p></li>
<li><p>Do not use very high number of filters such as [512, 1024, 2048]. There are only 50,000 images for you to train unlike ImageNet which has about a million.</p></li>
</ol>

<p>In short, read up section 4.2 in the <a href="https://arxiv.org/pdf/1512.03385.pdf" rel="nofollow noreferrer">deep residual network paper</a> and try to replicate the network. You may also read <a href="http://florianmuellerklein.github.io/wRN_vs_pRN/" rel="nofollow noreferrer">this</a> blog.</p>
Small validation accuracy ResNet 50
<p><a href="https://github.com/slavaglaps/ResNet_cifar10/blob/master/resnet.ipynb" rel="nofollow noreferrer">https://github.com/slavaglaps/ResNet_cifar10/blob/master/resnet.ipynb</a></p>

<p>This is my model trained in 100 epochs
Accuracy on similar models and similar data reaches 90%
What is my problem?
I think it's worth reducing the learning rate with the passage of the epochs.
What do you think that can help me?</p>