def _create_swin_transformer_v2_cr(variant, pretrained=False, **kwargs): @@ -863,6 +883,20 @@ def swin_v2_cr_small_224(pretrained=False, **kwargs): return _create_swin_transformer_v2_cr('swin_v2_cr_small_224', pretrained=pretrained, **model_kwargs) @register_model def swin_v...
More weights pushed to HF hub along with multi-weight support, including: regnet.py, rexnet.py, byobnet.py, resnetv2.py, swin_transformer.py, swin_transformer_v2.py, swin_transformer_v2_cr.py Swin Transformer models support feature extraction (NCHW feat maps for swinv2_cr_*, and NHWC ...
vision_transformer.py to vision_transformer.cpython-37.pyc byte-compiling build/bdist.linux-x86_64/egg/ppdet/modeling/backbones/esnet.py to esnet.cpython-37.pyc byte-compiling build/bdist.linux-x86_64/egg/ppdet/modeling/backbones/name_adapter.py to name_adapter.cpython-37.pyc byte-compiling...
This paper combines deep learning with continual learning and federated learning, proposing a federated continual learning comprehensive model based on the Swin Transformer model (SSPW224-LwF-3). Our datasets come from shared datasets and integrate data from other datasets based on a ratio principle ...
In addition, the diffusion models are developed based on Swinv2-Unet, a variant of Swin Transformer v2 [16], which allows the model to learn features from local to global. Finally, we evaluate our model on the MSCOCO, CUB and MM-CelebA-HQ datasets. The results show that the proposed ...
Swin Transformer models support feature extraction (NCHW feat maps for swinv2_cr_*, and NHWC for all others) and spatial embedding outputs. FocalNet (from https://github.com/microsoft/FocalNet) models and weights added with significant refactoring, feature extraction, no fixed resolution / ...
More weights pushed to HF hub along with multi-weight support, including: regnet.py, rexnet.py, byobnet.py, resnetv2.py, swin_transformer.py, swin_transformer_v2.py, swin_transformer_v2_cr.py Swin Transformer models support feature extraction (NCHW feat maps for swinv2_cr_*, and NHWC ...
More weights pushed to HF hub along with multi-weight support, including: regnet.py, rexnet.py, byobnet.py, resnetv2.py, swin_transformer.py, swin_transformer_v2.py, swin_transformer_v2_cr.py Swin Transformer models support feature extraction (NCHW feat maps for swinv2_cr_*, and NHWC ...
Swin Transformer models support feature extraction (NCHW feat maps for swinv2_cr_*, and NHWC for all others) and spatial embedding outputs. FocalNet (from https://github.com/microsoft/FocalNet) models and weights added with significant refactoring, feature extraction, no fixed resolution / sizing...
More weights pushed to HF hub along with multi-weight support, including: regnet.py, rexnet.py, byobnet.py, resnetv2.py, swin_transformer.py, swin_transformer_v2.py, swin_transformer_v2_cr.py Swin Transformer models support feature extraction (NCHW feat maps for swinv2_cr_*, and NHWC ...