ImageNet-1k
A large-scale image classification dataset containing 1000 categories and approximately 1.28 million training images. It is a commonly used benchmark in computer vision.
Timeline (last 90 days)
When DINOv3 ViT-H+/16 is pruned to 50% sparsity, removing 427 million parameters, ImageNet-1k linear probe accuracy drops by only 0.8%, without weight correction or retraining.
In the ImageNet-1k supervised training setting, reViT-B/16 trained from scratch reaches accuracy comparable to DeiT III while reducing stored parameters by about 70%.
All Facts (2)
When DINOv3 ViT-H+/16 is pruned to 50% sparsity, removing 427 million parameters, ImageNet-1k linear probe accuracy drops by only 0.8%, without weight correction or retraining.
50%UnverifiedIn the ImageNet-1k supervised training setting, reViT-B/16 trained from scratch reaches accuracy comparable to DeiT III while reducing stored parameters by about 70%.
50%