GSCNN
Semantic Segmentation Network
This code implements a neural network architecture designed to perform semantic segmentation in computer vision tasks.
Gated-Shape CNN for Semantic Segmentation (ICCV 2019)
920 stars
36 watching
202 forks
Language: Python
last commit: about 2 years ago
Linked from 1 awesome list
computer-visiondeep-learningiccv2019nv-tlabspytorchsemantic-boundariessemantic-segmentation
Related projects:
| Repository | Description | Stars |
|---|---|---|
| | Real-time semantic segmentation software for high-resolution images using a deep neural network architecture | 605 |
| | A PyTorch implementation of a deep learning model for semantic segmentation tasks in computer vision. | 380 |
| | Deep learning models for semantic segmentation of images | 101 |
| | Monorepo implementing PyTorch-based neural network architecture for image segmentation | 1,787 |
| | Deconvolution network architecture for semantic segmentation | 325 |
| | Implementations of deep learning architectures for semantic segmentation of images in various datasets. | 6 |
| | A Python implementation of a deep neural network architecture for semantic image segmentation | 48 |
| | An implementation of a deep learning model for semantic segmentation using a novel attention mechanism to capture long-range dependencies in images. | 1,432 |
| | An implementation of semantic segmentation using fully convolutional networks | 188 |
| | An implementation of fully convolutional neural networks for semantic segmentation using TensorFlow as the backend. | 15 |
| | Semantic segmentation using convolutional neural networks for aerial and satellite images | 260 |
| | A deep learning-based semantic segmentation pipeline using the Catalyst framework. | 20 |
| | Provides PyTorch implementations of various models and pipelines for semantic segmentation in deep learning. | 1,729 |
| | An implementation of a Cascaded Fully Convolutional Neural Network architecture for medical image segmentation | 304 |
| | A PyTorch implementation of FCN for semantic segmentation with an easy-to-use interface and pre-trained models. | 161 |