4 个仓库
Awesome GitHub RepositoriesSemantic Segmentation Architectures
Neural network structures that combine encoders and decoders to produce pixel-wise semantic labels for images.
Distinct from Encoder-Decoder Architectures: Focuses on pixel-level semantic segmentation rather than the sequence generation found in vision-text transformer architectures
Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Semantic Segmentation Architectures. Refine with filters or upvote what's useful.
Awesome Semantic Segmentation Architectures GitHub Repositories
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- facebookresearch/sapiens
facebookresearch/sapiens
5,388在 GitHub 上查看Sapiens 是一个高分辨率人体视觉模型，专为高精度、以人为中心的计算机视觉任务而设计。它是一套用于估计人体姿态、深度和表面几何形状的工具集。 该项目利用视觉 Transformer 主干网络通过共享编码器执行多项任务。这种架构能够同时预测骨骼结构、关节位置以及相机与人体对象之间的距离。 该模型的功能涵盖了人体部位分割（从背景中分离解剖区域）和表面法线预测（从 2D 图像中恢复 3D 几何细节）。这些任务由一个采用像素级回归和语义分割掩码的多任务学习框架提供支持。
Uses neural network structures to produce pixel-wise semantic labels for isolating human subjects.
Python - qubvel/segmentation_models
qubvel/segmentation_models
4,917在 GitHub 上查看This is an image segmentation framework and masking toolkit for constructing binary and multi-class neural network architectures. It serves as a deep learning encoder wrapper that integrates pre-trained convolutional neural network architectures into semantic segmentation models. The library enables the use of pre-trained backbones to isolate complex patterns and leverages transfer learning to accelerate training. It provides a collection of overlap-based loss functions and precision metrics specifically designed to evaluate and refine the accuracy of image masks. The toolkit covers the full
Implements encoder-decoder architectures specifically for pixel-wise semantic segmentation.
Pythondensenetefficientnetfpn - roboflow/sports
roboflow/sports
4,881在 GitHub 上查看Roboflow Sports is a sports video analysis system that combines object detection and tracking with bird's-eye field visualization. Its core pipeline detects and tracks players, referees, and balls across video frames, then maps those tracked positions onto a radar-style overhead view of the playing field. The system goes beyond basic detection by localizing field boundaries and key landmarks such as pitch lines and corners, enabling spatial mapping of player positions relative to the field geometry. It classifies detected players by team affiliation through visual feature extraction and clust
Classifies each pixel of video frames into field, background, or boundary categories using an encoder-decoder network.
Pythoncomputer-visiondeep-learningdeep-neural-networks - nvlabs/segformer
NVlabs/SegFormer
3,347在 GitHub 上查看SegFormer is a semantic segmentation framework and transformer-based model designed for pixel-level image classification. It provides a deep learning architecture that assigns class labels to pixels using a hierarchical transformer encoder and a multi-layer perceptron decoder. The framework utilizes a hierarchical transformer encoder to process multi-scale features through a pyramid of blocks and an all-MLP decoder to aggregate these features without complex attention mechanisms. It incorporates overlap patch embedding to preserve local continuity and sequential self-attention reduction to ma
Implements a deep learning architecture that assigns class labels to pixels using a hierarchical transformer encoder and MLP decoder.
Pythonade20kcityscapessemantic-segmentation
