JFT-300M is an internal Google dataset used for training image classification models. Images are labeled using an algorithm that uses complex mixture of raw web signals, connections between web-pages and user feedback. This results in over one billion la
抛开效果提升,对比其他自监督方法,潜在的优势是速度快、内存占用小、超参少,并且能复用NLP预训练框架和经验,方便把ViT扩展到更大规模,从而释放算力和(无标注)数据的威力。在解决被Google大规模内部标注数据集JFT-300M“卡脖子” 的问题上,自监督是唯一选项。
timm, pip install timm torch>=1.4.0 torchvision>=0.5.0 pyyaml T2T-ViT Models Test Test the T2T-ViT_t-14 (take transformer in T2T transformer), Download theT2T-ViT_t-14, then test it by running: CUDA_VISIBLE_DEVICES=0 python main.py path/to/data --model T2t_vit_t_14 -b 100 --...
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