The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet,
列出具有预训练权重的模型 我们可以简单看看,一共大概有400多个模型,我们都是可以随意使用的 import timmfrom pprint import pprintmodel_names = timm.list_models(pretrained=True)pprint(model_names)['adv_inception_v3','cait_m36_384','cait_m48_448','cait_s24_224','cait_s24_384','cait_s36_384'...
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'beit_base_patch16_384', 'beit_large_patch16_224', 'beit_large_patch16_224_in22k', 'beit_large_patch16_384', 'beit_large_patch16_512', 'botnet26t_256', 'cait_m36_384', 'cait_m48_448', 'cait_s24_224', 'cait_s24_384', 'cait_s36_384', 'cait_xs24_384...
21k pretrained -> 1k fine-tuned:tf_efficientnetv2_s/m/l_21ft1k v2 models w/ v1 scaling:tf_efficientnetv2_b0throughb3 Rename my prev V2 guessefficientnet_v2s->efficientnetv2_rw_s Some blankefficientnetv2_*models in-place for future native PyTorch training ...