ENet

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Implementations

TensorFlow-ENet

TensorFlow implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation.

This model was tested on the CamVid dataset with street scenes taken from Cambridge, UK. For more information on this dataset, please visit: http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/.

Requirements: TensorFlow >= r1.2

Model: https://github.com/kwotsin/TensorFlow-ENet/blob/master/enet.py

Github: https://github.com/kwotsin/TensorFlow-ENet

PyTorch-ENet

PyTorch (v1.0.0) implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation, ported from the lua-torch implementation ENet-training created by the authors.

This implementation has been tested on the CamVid and Cityscapes datasets. Currently, a pre-trained version of the model trained in CamVid and Cityscapes is available here.

Model: https://github.com/davidtvs/PyTorch-ENet/blob/master/models/enet.py

Github: https://github.com/davidtvs/PyTorch-ENet

Here is a repository, which contains multiple implementations of ENet by different languages

Github: https://github.com/mrgloom/awesome-semantic-segmentation

segmentationenet
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