ImageNet Classification with Deep Convolutional Neural Networks
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
Official documentation for an image-and-text model, including inputs, outputs, architecture dependencies and safety evaluation.
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
Compare disclosed capability limits with the use cases of generative image systems.
@misc{deepmind2026nano,
title = {Nano Banana 2.1 — Model Card},
author = {Google DeepMind},
year = {2026},
url = {https://deepmind.google/models/model-cards/nano-banana-2-1/}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
A generative model learns to reverse a gradual noise process, establishing an influential approach to image synthesis.
Contrastive training aligns images with natural-language descriptions, enabling transfer to new visual categories.