keras-deeplab-v3-plus
Semantic image segementation model
An implementation of Deeplabv3+ in Keras with pretrained weights and customization options for semantic image segmentation.
Keras implementation of Deeplab v3+ with pretrained weights
1k stars
27 watching
426 forks
Language: Jupyter Notebook
last commit: 5 months ago
Linked from 1 awesome list
Related projects:
Repository | Description | Stars |
---|---|---|
| An implementation of a deep learning architecture for image segmentation using the Keras framework. | 185 |
| An implementation of the SegNet-Basic deep learning model for image segmentation using Keras and convolutional neural networks. | 83 |
| An implementation of a state-of-the-art deep learning model for semantic image segmentation | 197 |
| An implementation of a deep learning model for image segmentation using Keras and dilated convolutions | 301 |
| An implementation of a deep neural network architecture for real-time semantic segmentation in Python. | 115 |
| Provides pre-trained and customizable semantic segmentation model in MATLAB | 23 |
| An implementation of SegNet architecture for semantic segmentation using tensorflow and keras. | 179 |
| This project demonstrates basic semantic segmentation using a deep neural network in Keras. | 56 |
| An implementation of a deep neural network architecture for image classification using Keras. | 469 |
| A high-performance PyTorch implementation of semantic image segmentation using a custom encoder-decoder architecture. | 334 |
| A Python implementation of a deep neural network architecture for semantic image segmentation | 48 |
| An open-source implementation of an image segmentation model that combines background removal and object detection capabilities. | 1,484 |
| Keras implementation of Fully Convolutional Networks for Semantic Segmentation | 650 |
| Represents an implementation of the Inception-ResNet v2 deep learning model in Keras. | 180 |
| An implementation of a deep learning-based image representation learning approach using a modified fully connected layer and transfer learning from VGG16 | 34 |