# Semantic Segmentation

UPerNet semantic segmentation model based on Swin Transformer architecture, suitable for high-precision image segmentation tasks

UPerNet semantic segmentation model based on Swin Transformer small architecture, suitable for scene parsing tasks like ADE20K

UPerNet is a semantic segmentation model based on the ConvNeXt-Tiny architecture, suitable for image segmentation tasks.

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

UPerNet is a semantic segmentation model based on the ConvNeXt-Small architecture, suitable for image segmentation tasks.

UPerNet image segmentation model based on ConvNeXt-Tiny encoder, suitable for semantic segmentation tasks

A PyTorch-based image segmentation model library supporting various encoder-decoder architectures for semantic segmentation tasks

This model is a fine-tuned image segmentation model based on Logiroad/route_background_semantic_x2 on the Logiroad/route_background_semantic dataset, primarily used for road background semantic segmentation tasks.

A PyTorch-based image segmentation model using Transformer architecture for dense prediction tasks

UPerNet is an image segmentation model implemented in PyTorch, supporting semantic segmentation tasks.

PAN is an image segmentation model implemented in PyTorch, utilizing pyramid attention mechanisms to enhance feature extraction capabilities

DeepLabV3 is a semantic segmentation model implemented in PyTorch, suitable for image segmentation tasks.

PSPNet is a deep learning model for semantic segmentation that uses pyramid pooling modules to capture multi-scale contextual information

Linknet is a PyTorch-implemented image segmentation model suitable for semantic segmentation tasks.

A PyTorch-based semantic segmentation model utilizing multi-scale attention mechanisms, suitable for image segmentation tasks

A semantic segmentation model based on PyTorch, utilizing an improved UNet++ architecture, suitable for image segmentation tasks.

Unet image segmentation model implemented in PyTorch, supporting multiple encoder architectures

A semantic segmentation model based on the Segformer architecture, optimized for the Cityscapes dataset

A lightweight semantic segmentation model based on Segformer architecture, pre-trained on the Cityscapes dataset

A semantic segmentation model based on the Segformer architecture, specifically optimized for the Cityscapes dataset

PyTorch-based Segformer model for semantic segmentation tasks, pre-trained on the ADE20K dataset

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

PyTorch-based Unet image segmentation model supporting multiple encoder architectures

A fine-tuned aerial drone image segmentation model based on nvidia/mit-b0, suitable for multi-class segmentation tasks in aerial images.

Image segmentation dataset for human parsing tasks

A lightweight image segmentation model based on SegFormer architecture, specifically fine-tuned for sidewalk scenarios

A semantic segmentation model based on MobileViTv2 architecture with DeepLabV3 head, pretrained on PASCAL VOC dataset at 512x512 resolution

A semantic segmentation model based on the MobileViTv2 architecture, pre-trained on the PASCAL VOC dataset, supporting 512x512 resolution image processing

A semantic segmentation model based on the Segformer architecture, used to assign semantic category labels to each pixel in an image.

UperNet is a framework for semantic segmentation that uses Swin Transformer as the backbone network, enabling efficient pixel-level semantic annotation.

UperNet is a framework for semantic segmentation, utilizing Swin Transformer as the backbone network to achieve pixel-level semantic label prediction.

UperNet is a semantic segmentation framework that uses Swin Transformer as the backbone network, enabling pixel-level semantic label prediction.

UperNet is a framework for semantic segmentation, utilizing ConvNeXt as the backbone network, capable of predicting semantic labels for each pixel.

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

UperNet is a framework for semantic segmentation that uses ConvNeXt as the backbone network and can predict semantic labels for each pixel.

UperNet is a framework for semantic segmentation that uses ConvNeXt as its backbone network, enabling pixel-level semantic label prediction.

UperNet is a framework for semantic segmentation that uses ConvNeXt as the backbone network, capable of predicting a semantic label for each pixel.

InLegalBERT is a Transformer model pre-trained on Indian legal texts, specializing in natural language processing tasks for the legal domain.

An image segmentation model fine-tuned on the imadd/water_dataset dataset based on nvidia/mit-b0, designed for water segmentation tasks