The exact arguments to reproduce the models presented in our paper can be found in the args column of the pretrained models section. This codebase has been developed with python version 3.6, PyTorch version 1.7.1, CUDA 11.0 and torchvision 0.8.2. Please install PyTorch and download the ImageNet dataset. load( 'facebookresearch/dino:main', 'dino_resnet50') Training Documentation load( 'facebookresearch/dino:main', 'dino_xcit_medium_24_p8') load( 'facebookresearch/dino:main', 'dino_xcit_medium_24_p16') load( 'facebookresearch/dino:main', 'dino_xcit_small_12_p8') load( 'facebookresearch/dino:main', 'dino_xcit_small_12_p16') load( 'facebookresearch/dino:main', 'dino_vitb8') load( 'facebookresearch/dino:main', 'dino_vitb16') of the Kingdom of SCS on the sequestrated branch, IV, no. load( 'facebookresearch/dino:main', 'dino_vits8') load( 'facebookresearch/dino:main', 'dino_vits16') We also release XCiT models ( ) trained with DINO: arch Note that DeiT-S and ViT-S names refer exactly to the same architecture. We also provide the backbone in onnx format, as well as detailed arguments and training/evaluation logs. You can choose to download only the weights of the pretrained backbone used for downstream tasks, or the full checkpoint which contains backbone and projection head weights for both student and teacher networks. Odluka o utvrivanju Osnovne liste lijekova Hrvatskog zavoda za zdravstveno osiguranje.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |