Pixellib is a library for performing segmentation of images and videos. It supports the two major types of image segmentation:
You can implement both semantic and instance segmentation with few lines of code.
There are two types of Deeplabv3+ models available for performing semantic segmentation with PixelLib:
- Deeplabv3+ model with xception as network backbone trained on Ade20k dataset, a dataset with 150 classes of objects.
- Deeplabv3+ model with xception as network backbone trained on Pascalvoc dataset, a dataset with 20 classes of objects.
Instance segmentation is implemented with PixelLib by using Mask R-CNN model trained on coco dataset.
Note Deeplab and mask r-ccn models are available in the release of this repository.
Install latest version of tensorflow(Tensorflow 2.0+) with:
pip3 install tensorflow
Install Pixellib with:
pip3 install pixellib --upgrade
Visit PixelLib's official documentation on readthedocs
Read the following tutorials on performing both semantic and instance segmentation of images and videos with PixelLib.
Bonlime, Keras implementation of Deeplab v3+ with pretrained weights https://github.com/bonlime/keras-deeplab-v3-plus
Liang-Chieh Chen. et al, Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation https://arxiv.org/abs/1802.02611
Matterport, Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow https://github.com/matterport/Mask_RCNN
Kaiming He et al, Mask R-CNN https://arxiv.org/abs/1703.06870
TensorFlow DeepLab Model Zoo https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md
Pascalvoc and Ade20k datasets' colormaps https://github.com/tensorflow/models/blob/master/research/deeplab/utils/get_dataset_colormap.py