Semantic segmentation tensorflow
Semantic segmentation task for ADE20k & cityscapse dataset, based on several models.
This is a Tensorflow implementation of semantic segmentation models on [MIT ADE20K scene parsing dataset](https://github.com/hangzhaomit/semantic-segmentation-pytorch) and [Cityscapes dataset](https://www.cityscapes-dataset.com/benchmarks/) The project is written primarily in Python, first published in 2018. Key topics include: ade20k, cityscapes, enet, fcn-8s, icnet.
semantic-segmentation-tensorflow
This is a Tensorflow implementation of semantic segmentation models on MIT ADE20K scene parsing dataset and Cityscapes dataset
We re-produce the inference phase of several models, including PSPNet, FCN, and ICNet by transforming the released pre-trained weights into tensorflow format, and apply on handcraft models. Also, we refer to ENet from freg856 github. Still working on task integrated.
Models
...to be continue
Install
Get corresponding transformed pre-trained weights, and put into model directory:
| FCN | PSPNet | ICNet |
|---|---|---|
| Google drive | Google drive | Google drive |
Inference
Run following command:
python inference.py --img-path /Path/To/Image --dataset Model_Type
Arg list
--model - choose from "icnet"/"pspnet"/"fcn"/"enet"
Import module in your code:
pythonfrom model import FCN8s, PSPNet50, ICNet, ENet model = PSPNet50() # or another model model.read_input(img_path) # read image data from path sess = tf.Session(config=config) init = tf.global_variables_initializer() sess.run(init) model.load(model_path, sess) # load pretrained model preds = model.forward(sess) # Get prediction
Results
ade20k
| Input Image | PSPNet | FCN |
|---|---|---|
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cityscapes
| Input Image | ICNet | ENet |
|---|---|---|
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Citation
@inproceedings{zhao2017pspnet,
author = {Hengshuang Zhao and
Jianping Shi and
Xiaojuan Qi and
Xiaogang Wang and
Jiaya Jia},
title = {Pyramid Scene Parsing Network},
booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2017}
}
Scene Parsing through ADE20K Dataset. B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso and A. Torralba. Computer Vision and Pattern Recognition (CVPR), 2017. (http://people.csail.mit.edu/bzhou/publication/scene-parse-camera-ready.pdf)
@inproceedings{zhou2017scene,
title={Scene Parsing through ADE20K Dataset},
author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2017}
}
Semantic Understanding of Scenes through ADE20K Dataset. B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso and A. Torralba. arXiv:1608.05442. (https://arxiv.org/pdf/1608.05442.pdf)
@article{zhou2016semantic,
title={Semantic understanding of scenes through the ade20k dataset},
author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},
journal={arXiv preprint arXiv:1608.05442},
year={2016}
}
Contributors
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