# High-Precision Segmentation

UPerNet semantic segmentation model based on ConvNeXt-Large encoder, suitable for scene parsing tasks like ADE20K

UPerNet image segmentation model based on ConvNeXt architecture, suitable for semantic segmentation tasks

This paper proposes a method to reinterpret Vision Transformer (ViT) as an image segmentation model, demonstrating ViT's potential in image segmentation tasks.

This paper proposes a method to reinterpret Vision Transformer (ViT) as an image segmentation model, revealing ViT's potential in image segmentation tasks.

This paper proposes an image segmentation model based on Vision Transformer (ViT), revealing the potential of ViT in image segmentation tasks.

The model proposed in this paper reveals the potential of Vision Transformer (ViT) in image segmentation tasks.

This paper proposes a novel perspective by treating Vision Transformer (ViT) as an image segmentation model and explores its potential in image segmentation tasks.

This model is developed based on the paper 'Your ViT is Actually an Image Segmentation Model' and is a Vision Transformer model for image segmentation tasks.

This model reveals the potential of Vision Transformer (ViT) in image segmentation tasks by transforming ViT into an efficient image segmentation model through specific methods.

This model reveals the potential of Vision Transformer (ViT) in image segmentation tasks by adapting its architecture for segmentation purposes.

This paper proposes a method to reinterpret Vision Transformer (ViT) as an image segmentation model, demonstrating ViT's potential in image segmentation tasks.

By rethinking the architecture of Vision Transformer (ViT), this model demonstrates its potential in image segmentation tasks.

This is an image segmentation model based on the Transformer architecture, specifically designed for page layout and floor plan segmentation tasks.

A fine-tuned semantic segmentation model based on the Mask2Former framework, suitable for road scene understanding and autonomous driving applications

PyTorch-based Segformer image segmentation model, suitable for semantic segmentation tasks on the ADE20K dataset

A document layout detection model based on the DETR architecture, capable of identifying various layout elements in documents.

An image segmentation model fine-tuned on graffiti segmentation datasets based on NVIDIA's SegFormer-B0 architecture, capable of accurately identifying and segmenting graffiti areas in images.

A crack segmentation model based on the SegFormer architecture, fine-tuned on a crack segmentation dataset for detecting crack structures in images

This is an instance segmentation model trained on the YOLOv8s architecture, specifically designed for detecting and segmenting solar panel images.

A semantic segmentation model fine-tuned on stamp verification dataset based on nvidia/mit-b0, used for precise segmentation of stamp regions in images

UperNet is a semantic segmentation framework combined with the ConvNeXt large backbone network for pixel-level semantic label prediction.

This SegFormer model has been fine-tuned on the SegmentsAI sidewalk-semantic dataset for semantic segmentation tasks.

An image segmentation model fine-tuned on the inStryde/inStrydeSegmentationFoot dataset, based on nvidia/mit-b5