# High-Resolution Image Classification

MambaVision is the first hybrid computer vision model combining the strengths of Mamba and Transformer. It enhances visual feature modeling by redesigning the Mamba formulation and incorporates self-attention modules in the final layers of the Mamba architecture to improve long-range spatial dependency modeling.

The first hybrid computer vision model combining the advantages of Mamba and Transformer, enhancing visual feature modeling capability by reconstructing the Mamba formula

EfficientNet is an efficient convolutional neural network that achieves high-performance image classification by uniformly scaling depth, width, and resolution

Swin Transformer v2 is a vision Transformer model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at 384x384 resolution, featuring hierarchical feature maps and local window self-attention mechanisms.

Swin Transformer v2 is a vision Transformer model that achieves efficient image classification and dense recognition tasks through hierarchical feature maps and local window self-attention mechanisms.

Swin Transformer v2 is a vision Transformer model that achieves efficient image classification through hierarchical feature maps and local window-based self-attention mechanisms.

Swin Transformer v2 is a vision Transformer model that achieves efficient image classification and dense recognition tasks through hierarchical feature maps and local window self-attention mechanisms.

Swin Transformer v2 is a vision Transformer model pre-trained on ImageNet-1k, featuring hierarchical feature maps and local window self-attention mechanisms with linear computational complexity.

CvT-21 is a vision model combining convolutional and Transformer architectures, pretrained on ImageNet-22k and fine-tuned on ImageNet-1k

CvT-13 is a vision transformer model pre-trained on the ImageNet-1k dataset, improving the performance of traditional vision transformers by introducing convolutional operations.

Swin Transformer is a hierarchical vision Transformer based on shifted windows, specifically designed for image classification tasks.