<p>It is likely to be possible, but we can't exactly know before trying.</p>
<p>You may try using SOTA(State of the art) or near-SOTA Convolution neural network architectures such as <a href="https://arxiv.org/abs/1905.11946" rel="nofollow noreferrer">EfficientNet</a> or <a href="https://arxiv.org/abs/2004.08955" rel="nofollow noreferrer">ResNeSt</a>.</p>
<p>There are better Preprocessing methods than the standard ones in the usual Deep Learning library such as <a href="https://research.google/pubs/pub47890/" rel="nofollow noreferrer">AutoAugment</a>, but they are usually not necessary unless you want the improve the performance to the extreme or are training with very small datasets.</p>
<p>Also, training with larger datasets are definitely going to help.</p>
Can a Neural Network Image Classifier distinguish between similar classes?
<p>I have been training an image classifier using Keras, and have tried out various convnet architectures. The dataset consists of jars of food. The problem is that many of the classes are extremely similar, with the differences between them often just being a slightly different label and color. Another problem is that the images are taken under different lighting conditions, so even color is often an ineffective means of distinguishing them. Is there a good  network architecture, or some preprocessing, which would be able to increase the accuracy of a classifier?</p>