Image classification, or, for that matter, any classification task, is inherently a supervised learning task. Supervised tasks learn about the different classes through the underlying training sets available.
Even though CNNs are optimized feed forward networks that share weights, the number of parameters to train in a deep ConvNet might be huge. This is one of the reasons why huge training sets are required to achieve better performing networks. Luckily, research groups across the globe have been working towards collecting, hand-annotating, and crowdsourcing different datasets. These datasets are utilized to benchmark performance of different algorithms, as well as to identify winners in different competitions.
The following is a brief listing of widely accepted benchmarking datasets in the field of image classification:
- ImageNet: With over 14 million hand...
