DeepLab-Context
Image Segmentation System
An implementation of a deep learning system for semantic image segmentation using a combination of convolutional neural networks and conditional random fields.
239 stars
13 watching
97 forks
Language: C++
last commit: about 9 years ago
Linked from 1 awesome list
Related projects:
| Repository | Description | Stars |
|---|---|---|
| | Implementations of deep learning architectures for semantic segmentation of images in various datasets. | 6 |
| | A system that uses large language models to generate segmentation masks for images based on complex queries and world knowledge. | 1,923 |
| | Trains DeepLab model for semantic image segmentation using annotated data and various training procedures | 172 |
| | A high-performance PyTorch implementation of semantic image segmentation using a custom encoder-decoder architecture. | 334 |
| | An implementation of a deep learning network for image segmentation tasks using Lua and the Torch7 framework. | 168 |
| | An open-source implementation of the SegNet deep learning architecture for image segmentation | 1,080 |
| | An open-source implementation of an image segmentation model that combines background removal and object detection capabilities. | 1,484 |
| | A convolutional neural network architecture for biomedical image segmentation | 430 |
| | Deep learning models for semantic segmentation of images | 101 |
| | A deep learning-based semantic segmentation pipeline using the Catalyst framework. | 20 |
| | An implementation of Deeplabv3+ in Keras with pretrained weights and customization options for semantic image segmentation. | 1,360 |
| | An implementation of a deep learning model for separating foreground and background in images | 626 |
| | An implementation of deep image matting in PyTorch using a neural network architecture. | 821 |
| | Provides tools and data for developing image segmentation models with diverse object categories. | 463 |
| | Provides tools and models for training deep neural networks for real-time semantic segmentation and scene parsing | 351 |