4 مستودعات
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
5,388عرض على GitHubSapiens هو نموذج رؤية بشرية عالي الدقة مصمم لمهام رؤية الكمبيوتر التي تركز على الإنسان بدقة عالية. يعمل كمجموعة من الأدوات لتقدير وضعية الإنسان، والعمق، وهندسة السطح. يستخدم المشروع هيكل vision transformer لأداء مهام متعددة من خلال مشفر مشترك. تتيح هذه البنية التنبؤ المتزامن بالهياكل الهيكلية، ومواقع المفاصل، والمسافة بين الكاميرا والموضوع البشري. تغطي قدرات النموذج تجزئة أجزاء جسم الإنسان لعزل المناطق التشريحية عن الخلفيات والتنبؤ بوضع السطح لاستعادة التفاصيل الهندسية ثلاثية الأبعاد من الصور ثنائية الأبعاد. يتم دعم هذه المهام من خلال إطار عمل للتعلم متعدد المهام يستخدم الانحدار على مستوى البكسل وقناع التجزئة الدلالي.
Uses neural network structures to produce pixel-wise semantic labels for isolating human subjects.
Python - qubvel/segmentation_models
qubvel/segmentation_models
4,917عرض على GitHubThis 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
4,881عرض على GitHubRoboflow 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
3,347عرض على GitHubSegFormer 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
