<p>This is a really good question. I was wondering about the same and played around with some of the models that are on <a href="https://tfhub.dev/s?module-type=image-classification" rel="nofollow noreferrer">TensorFlow Hub</a>. So, here are my two cents.</p>

<p>The current best models in terms of performance on ImageNet are the ones which are obtained with <a href="https://arxiv.org/abs/1712.00559" rel="nofollow noreferrer">Progressive Neural Architecture Search</a>. On the other hand, these models are incredibly slow to train because they are huge. When it comes to the models such as InceptionNet, ResNet, and VGG, this is a <a href="https://github.com/jcjohnson/cnn-benchmarks" rel="nofollow noreferrer">good link</a> to check out the performance compared to the training/inference speed.</p>

<p>My personal experience is that if you want to maximize performance, use <a href="https://tfhub.dev/google/imagenet/resnet_v2_152/classification/3" rel="nofollow noreferrer">ResNet152</a>. If you want a relatively fast CNN, while achieving good performance, go with <a href="https://tfhub.dev/google/imagenet/resnet_v2_50/classification/3" rel="nofollow noreferrer">ResNet50</a>. When it comes to the VGG nets, I played around with the <a href="https://github.com/tensorflow/models/blob/master/research/slim/nets/vgg.py" rel="nofollow noreferrer">TF-Slim implementation</a> but it was slower than ResNet50, with performance around the same. Finally, I can't say much about Inception because I didn't use it. In the end, I went with ResNet152, because it yield the best performance for me (Please note that I was using a pre-trained version and I was fine-tuning it to my task).</p>

<p>To summarize, I think that there is no general <strong>best CNN</strong>. I would avoid using VGG16/19, because it yields worse performance than ResNet50, while being slower. If you have access to a lot of computational power, go with Resnet152 or PNASNet. Again, this my opinion based on my personal experience by playing around with the pre-trained models on TF-Hub.</p>
What is recognised as the best image classification neural network for 2018?
<p>Prior to 2017, it was relatively simple to understand which CNN was the best to classify images with the imagnet yearly competition.</p>

<p><a href="https://i.stack.imgur.com/l86pf.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/l86pf.png" alt="enter image description here"></a></p>

<p>In 2017 the imagenet competition was divided into different <a href="http://image-net.org/challenges/LSVRC/2017/results" rel="nofollow noreferrer">tasks</a> with winners such as <a href="https://arxiv.org/abs/1709.01507" rel="nofollow noreferrer">this</a>.  In 2018, the competition moved to kaggle and became about 3D detection.  </p>

<p>I am interested in image classification only and there no longer seems to be a competition for this.</p>

<p>Does anyone know what neural network was recognised as the best for image classification in 2018?</p>