A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets

Small Images, Big Science: Fast ImageNet for Quick Experiments

Big image collections like ImageNet help teach computers to see, but they take a lot of time and money to use.
Researchers made a version where every photo is smaller — a downsampled set — yet keeps the same classes and number of images as the full collection.
That means you get the same variety of objects, but the files are tiny so training runs are much faster.
You can test ideas, try new designs, and tune settings without waiting days or spending lots.
Results from these tiny images behave similar to the big ones, so many choices you make transfer back to the full dataset.
This is great for students, hobbyists, or teams who want quick feedback and lower cost.
The smaller sets come in a few sizes, so you pick what fits your computer and time.
Try it if you want to explore image models quickly — it speeds things up, keeps the challenge, and saves resources so more people can play and learn with real data.

Read article comprehensive review in Paperium.net:
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets

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