The performance of a visual representation learning system is largely influenced by the three main factors:the neural network architecture,the method used for training the network,and the data used for training。在视觉识别领域,每个领域的进步都有助于整体性能的提高。 神经网络体系结构设计的创新一直在表...
Table 3.Co-design matters.When the architecture and the learning framework are co-designed and used together, masked image pre-training becomes effective for ConvNeXt. We report the finetuning performance from 800 epoch FCMAE pre-trained models. The relative improvement is bigger with a larger mo...
这支持了前面提到的模型和学习框架应该一起考虑(both the model and learning framework should be considered together)的想法,特别是在涉及到自监督学习时。 Table 3. Co-design matters. When the architecture and the learning framework are co-designed and used together, masked image pre-training becomes ef...
这支持了前面提到的模型和学习框架应该一起考虑(both the model and learning framework should be considered together)的想法,特别是在涉及到自监督学习时。 Co-design matters.When the architecture and the learning framework are co-designed and used together, masked image pre-training becomes effective for ...
The best accuracy of 99.5% was achieved when three MRI sequences were input as three channels of the pre-trained CNN.#The study demonstrated the efficacy of the representations learned by a modern CNN architecture, which has a higher inductive bias for the image data than vision transformers ...
().__init__() cfg = get_config(cfg_file) self.backbone = build_backbone(cfg.model.architecture) self.head = build_head(cfg.model.head) def forward(self, x): x = self.backbone(x) x = self.head(x) return x cfg_file = "configs/convnext/convnext_tiny_224.yaml" m = Model(cfg...
比如,视觉 Transformer 的架构设计一般来将遵循一个 meta architecture,就是虽然 token mixer 的类型会有不同 (Self-attention,Spatial MLP,Window-based Self-attention 等等),但是基本的宏观架构一般都是由4个 stage 组成的金字塔架构。...
ConvNets 和 Hierarchical Transformers 都具备相似的归纳偏置,但在训练过程和宏/微观层次的架构设计 (macro/micro-level architecture design) 上有显著的差异。在这项工作中,作者重新检查和审视了卷积模型的设计空间,这项研究旨在弥合 pre-ViT 时代和 post-ViT 时代模型性能上的差距,并想要 探索出一个纯的卷积网络...
(GRN) layer that can be added to the ConvNeXt architecture to enhance inter-channel feature competition. This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets...
Figure 1. ConvNext architecture. Figure 2. (a) The internal structure of Stem Block. (b) The internal structure of Downsample Block. (c) The internal structure of ConvNext Block. The Stem Block in Figure 1 processes the input images via a patchify structure composed by a convolutional ...