Swin Transformer models support feature extraction (NCHW feat maps forswinv2_cr_*, and NHWC for all others) and spatial embedding outputs. FocalNet (fromhttps://github.com/microsoft/FocalNet) models and weights added with significant refactoring, feature extraction, no fixed resolution / sizing co...
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...
In contrast to Imagen, we focus on improving super-resolution diffusion models. We introduce a new UNet variant to our super-resolution diffusion model, called Swinv2-UNet. The Swin Transformer Block is replaced with the Swin Transformer v2 Block based on the original Swin-Unet [63], the comp...
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...
Investigators from Shanghai University Zero in on Networks (A Fusion Deraining Network Based On Swin Transformer and Convolutional Neural Network)ShanghaiPeople’s Republic of ChinaAsiaConvolutional NetworkEmerging TechnologiesMachine LearningNetworksNeural Networks...
In this article, we propose a Transformer-based contour-aware depth estimation module to recover the scene depth with the aid of the enhanced perception of object contours. Besides, we develop a cascaded multiscale fusion module to aggregate multilevel features, where we combine the global context...
This paper presents a novel multi-class forgery detection approach that combines spatial-frequency fusion with Swin-Transformer to enhance robustness against image compression attacks. We develop an ...
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...
Swin Transformer models support feature extraction (NCHW feat maps forswinv2_cr_*, and NHWC for all others) and spatial embedding outputs. FocalNet (fromhttps://github.com/microsoft/FocalNet) models and weights added with significant refactoring, feature extraction, no fixed resolution / sizing co...
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 ...