tensorflow · Masuell · May 12, 2023 · May 14, 2023 · May 14, 2023 · May 14, 2023
@@ -1,3 +1,9 @@
+# This is my fork of official Tensorflow repository. 
+For my work with DeepLab and RUGD dataset, please go to research/deeplab where is  more informations
+#
+#
+#
+
 <div align="center">
   <img src="https://storage.googleapis.com/tf_model_garden/tf_model_garden_logo.png">
 </div>

@@ -1,325 +1,14 @@
-# DeepLab: Deep Labelling for Semantic Image Segmentation
+# Welcome to my fork of official DeepLab repository
+I uploaded my new files and changes of official DeepLab files here. For official DeepLab README please see README_original.md <br />
 
-**To new and existing DeepLab users**: We have released a unified codebase for
-dense pixel labeling tasks in TensorFlow2 at https://github.com/google-research/deeplab2.
-Please consider switching to the newer codebase for better support. 
+This repository contains my bachelor thesis work. The aim of the thesis was to make a semantic segmentation model which could recognize various types of surfaces in nature. The robots of BUT's researchers (https://www.vut.cz/en/) maybe will use my trained model to identify if the robot can get through some type of terrain.<br />
 
-DeepLab is a state-of-art deep learning model for semantic image segmentation,
-where the goal is to assign semantic labels (e.g., person, dog, cat and so on)
-to every pixel in the input image. Current implementation includes the following
-features:
+To train the model, I used RUGD dataset (http://rugd.vision/).<br />
+To see some segmented examples of RUGD dataset with trained model, please go to datasets/rugd/exp/train_on_trainval_set/vis/segmentation_results/<br />
+After some small changes (comment or uncomment) in test_video.py and test_video2.py you can try trained model with your own images or video.<br />
+video_z_datasetu.mp4 is video of test ride with BUT's robot. <br />
+If you want to use RUGD dataset to train the model with 8 bit images, you can use color palette RUGD_vsetky_farby.pal with add_palete.bat to have all images with the same colormap.<br />
+vis.py is my modified version of vis_orig.py(which is vis.py but renamed).<br />
+There are some new files in utils repository - get_dataset_colormap.py and save_annotation.py. They contain code to work with RUGD dataset.<br />
+In datasets, there are 2 modified files - data_generator.py and build_voc2012_data_Vlastne_obrazky.py - which are used to make TFrecord of RUGD.
 
-1.  DeepLabv1 [1]: We use *atrous convolution* to explicitly control the
-    resolution at which feature responses are computed within Deep Convolutional
-    Neural Networks.
-
-2.  DeepLabv2 [2]: We use *atrous spatial pyramid pooling* (ASPP) to robustly
-    segment objects at multiple scales with filters at multiple sampling rates
-    and effective fields-of-views.
-
-3.  DeepLabv3 [3]: We augment the ASPP module with *image-level feature* [5, 6]
-    to capture longer range information. We also include *batch normalization*
-    [7] parameters to facilitate the training. In particular, we applying atrous
-    convolution to extract output features at different output strides during
-    training and evaluation, which efficiently enables training BN at output
-    stride = 16 and attains a high performance at output stride = 8 during
-    evaluation.
-
-4.  DeepLabv3+ [4]: We extend DeepLabv3 to include a simple yet effective
-    decoder module to refine the segmentation results especially along object
-    boundaries. Furthermore, in this encoder-decoder structure one can
-    arbitrarily control the resolution of extracted encoder features by atrous
-    convolution to trade-off precision and runtime.
-
-If you find the code useful for your research, please consider citing our latest
-works:
-
-*   DeepLabv3+:
-
-```
-@inproceedings{deeplabv3plus2018,
-  title={Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
-  author={Liang-Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam},
-  booktitle={ECCV},
-  year={2018}
-}
-```
-
-*   MobileNetv2:
-
-```
-@inproceedings{mobilenetv22018,
-  title={MobileNetV2: Inverted Residuals and Linear Bottlenecks},
-  author={Mark Sandler and Andrew Howard and Menglong Zhu and Andrey Zhmoginov and Liang-Chieh Chen},
-  booktitle={CVPR},
-  year={2018}
-}
-```
-
-*   MobileNetv3:
-
-```
-@inproceedings{mobilenetv32019,
-  title={Searching for MobileNetV3},
-  author={Andrew Howard and Mark Sandler and Grace Chu and Liang-Chieh Chen and Bo Chen and Mingxing Tan and Weijun Wang and Yukun Zhu and Ruoming Pang and Vijay Vasudevan and Quoc V. Le and Hartwig Adam},
-  booktitle={ICCV},
-  year={2019}
-}
-```
-
-*  Architecture search for dense prediction cell:
-
-```
-@inproceedings{dpc2018,
-  title={Searching for Efficient Multi-Scale Architectures for Dense Image Prediction},
-  author={Liang-Chieh Chen and Maxwell D. Collins and Yukun Zhu and George Papandreou and Barret Zoph and Florian Schroff and Hartwig Adam and Jonathon Shlens},
-  booktitle={NIPS},
-  year={2018}
-}
-
-```
-
-*  Auto-DeepLab (also called hnasnet in core/nas_network.py):
-
-```
-@inproceedings{autodeeplab2019,
-  title={Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic
-Image Segmentation},
-  author={Chenxi Liu and Liang-Chieh Chen and Florian Schroff and Hartwig Adam
-  and Wei Hua and Alan Yuille and Li Fei-Fei},
-  booktitle={CVPR},
-  year={2019}
-}
-
-```
-
-
-In the current implementation, we support adopting the following network
-backbones:
-
-1.  MobileNetv2 [8] and MobileNetv3 [16]: A fast network structure designed
-    for mobile devices.
-
-2.  Xception [9, 10]: A powerful network structure intended for server-side
-    deployment.
-
-3.  ResNet-v1-{50,101} [14]: We provide both the original ResNet-v1 and its
-    'beta' variant where the 'stem' is modified for semantic segmentation.
-
-4.  PNASNet [15]: A Powerful network structure found by neural architecture
-    search.
-
-5.  Auto-DeepLab (called HNASNet in the code): A segmentation-specific network
-    backbone found by neural architecture search.
-
-This directory contains our TensorFlow [11] implementation. We provide codes
-allowing users to train the model, evaluate results in terms of mIOU (mean
-intersection-over-union), and visualize segmentation results. We use PASCAL VOC
-2012 [12] and Cityscapes [13] semantic segmentation benchmarks as an example in
-the code.
-
-Some segmentation results on Flickr images:
-<p align="center">
-    <img src="g3doc/img/vis1.png" width=600></br>
-    <img src="g3doc/img/vis2.png" width=600></br>
-    <img src="g3doc/img/vis3.png" width=600></br>
-</p>
-
-## Contacts (Maintainers)
-
-*   Liang-Chieh Chen, github: [aquariusjay](https://github.com/aquariusjay)
-*   YuKun Zhu, github: [yknzhu](https://github.com/YknZhu)
-*   George Papandreou, github: [gpapan](https://github.com/gpapan)
-*   Hui Hui, github: [huihui-personal](https://github.com/huihui-personal)
-*   Maxwell D. Collins, github: [mcollinswisc](https://github.com/mcollinswisc)
-*   Ting Liu: github: [tingliu](https://github.com/tingliu)
-
-## Tables of Contents
-
-Demo:
-
-*   <a href='https://colab.sandbox.google.com/github/tensorflow/models/blob/master/research/deeplab/deeplab_demo.ipynb'>Colab notebook for off-the-shelf inference.</a><br>
-
-Running:
-
-*   <a href='g3doc/installation.md'>Installation.</a><br>
-*   <a href='g3doc/pascal.md'>Running DeepLab on PASCAL VOC 2012 semantic segmentation dataset.</a><br>
-*   <a href='g3doc/cityscapes.md'>Running DeepLab on Cityscapes semantic segmentation dataset.</a><br>
-*   <a href='g3doc/ade20k.md'>Running DeepLab on ADE20K semantic segmentation dataset.</a><br>
-
-Models:
-
-*   <a href='g3doc/model_zoo.md'>Checkpoints and frozen inference graphs.</a><br>
-
-Misc:
-
-*   Please check <a href='g3doc/faq.md'>FAQ</a> if you have some questions before reporting the issues.<br>
-
-## Getting Help
-
-To get help with issues you may encounter while using the DeepLab Tensorflow
-implementation, create a new question on
-[StackOverflow](https://stackoverflow.com/) with the tag "tensorflow".
-
-Please report bugs (i.e., broken code, not usage questions) to the
-tensorflow/models GitHub [issue
-tracker](https://github.com/tensorflow/models/issues), prefixing the issue name
-with "deeplab".
-
-## License
-
-All the codes in deeplab folder is covered by the [LICENSE](https://github.com/tensorflow/models/blob/master/LICENSE)
-under tensorflow/models. Please refer to the LICENSE for details.
-
-## Change Logs
-
-### March 26, 2020
-* Supported EdgeTPU-DeepLab and EdgeTPU-DeepLab-slim on Cityscapes.
-**Contributor**: Yun Long.
-
-### November 20, 2019
-* Supported MobileNetV3 large and small model variants on Cityscapes.
-**Contributor**: Yukun Zhu.
-
-
-### March 27, 2019
-
-* Supported using different loss weights on different classes during training.
-**Contributor**: Yuwei Yang.
-
-
-### March 26, 2019
-
-* Supported ResNet-v1-18. **Contributor**: Michalis Raptis.
-
-
-### March 6, 2019
-
-* Released the evaluation code (under the `evaluation` folder) for image
-parsing, a.k.a. panoptic segmentation. In particular, the released code supports
-evaluating the parsing results in terms of both the parsing covering and
-panoptic quality metrics. **Contributors**: Maxwell Collins and Ting Liu.
-
-
-### February 6, 2019
-
-* Updated decoder module to exploit multiple low-level features with different
-output_strides.
-
-### December 3, 2018
-
-* Released the MobileNet-v2 checkpoint on ADE20K.
-
-
-### November 19, 2018
-
-* Supported NAS architecture for feature extraction. **Contributor**: Chenxi Liu.
-
-* Supported hard pixel mining during training.
-
-
-### October 1, 2018
-
-* Released MobileNet-v2 depth-multiplier = 0.5 COCO-pretrained checkpoints on
-PASCAL VOC 2012, and Xception-65 COCO pretrained checkpoint (i.e., no PASCAL
-pretrained).
-
-
-### September 5, 2018
-
-* Released Cityscapes pretrained checkpoints with found best dense prediction cell.
-
-
-### May 26, 2018
-
-* Updated ADE20K pretrained checkpoint.
-
-
-### May 18, 2018
-* Added builders for ResNet-v1 and Xception model variants.
-* Added ADE20K support, including colormap and pretrained Xception_65 checkpoint.
-* Fixed a bug on using non-default depth_multiplier for MobileNet-v2.
-
-
-### March 22, 2018
-
-* Released checkpoints using MobileNet-V2 as network backbone and pretrained on
-PASCAL VOC 2012 and Cityscapes.
-
-
-### March 5, 2018
-
-* First release of DeepLab in TensorFlow including deeper Xception network
-backbone. Included checkpoints that have been pretrained on PASCAL VOC 2012
-and Cityscapes.
-
-## References
-
-1.  **Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs**<br />
-    Liang-Chieh Chen+, George Papandreou+, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille (+ equal
-    contribution). <br />
-    [[link]](https://arxiv.org/abs/1412.7062). In ICLR, 2015.
-
-2.  **DeepLab: Semantic Image Segmentation with Deep Convolutional Nets,**
-    **Atrous Convolution, and Fully Connected CRFs** <br />
-    Liang-Chieh Chen+, George Papandreou+, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille (+ equal
-    contribution). <br />
-    [[link]](http://arxiv.org/abs/1606.00915). TPAMI 2017.
-
-3.  **Rethinking Atrous Convolution for Semantic Image Segmentation**<br />
-    Liang-Chieh Chen, George Papandreou, Florian Schroff, Hartwig Adam.<br />
-    [[link]](http://arxiv.org/abs/1706.05587). arXiv: 1706.05587, 2017.
-
-4.  **Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation**<br />
-    Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, Hartwig Adam.<br />
-    [[link]](https://arxiv.org/abs/1802.02611). In ECCV, 2018.
-
-5.  **ParseNet: Looking Wider to See Better**<br />
-    Wei Liu, Andrew Rabinovich, Alexander C Berg<br />
-    [[link]](https://arxiv.org/abs/1506.04579). arXiv:1506.04579, 2015.
-
-6.  **Pyramid Scene Parsing Network**<br />
-    Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia<br />
-    [[link]](https://arxiv.org/abs/1612.01105). In CVPR, 2017.
-
-7.  **Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate shift**<br />
-    Sergey Ioffe, Christian Szegedy <br />
-    [[link]](https://arxiv.org/abs/1502.03167). In ICML, 2015.
-
-8.  **MobileNetV2: Inverted Residuals and Linear Bottlenecks**<br />
-    Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen<br />
-    [[link]](https://arxiv.org/abs/1801.04381). In CVPR, 2018.
-
-9.  **Xception: Deep Learning with Depthwise Separable Convolutions**<br />
-    François Chollet<br />
-    [[link]](https://arxiv.org/abs/1610.02357). In CVPR, 2017.
-
-10. **Deformable Convolutional Networks -- COCO Detection and Segmentation Challenge 2017 Entry**<br />
-    Haozhi Qi, Zheng Zhang, Bin Xiao, Han Hu, Bowen Cheng, Yichen Wei, Jifeng Dai<br />
-    [[link]](http://presentations.cocodataset.org/COCO17-Detect-MSRA.pdf). ICCV COCO Challenge
-    Workshop, 2017.
-
-11. **Tensorflow: Large-Scale Machine Learning on Heterogeneous Distributed Systems**<br />
-    M. Abadi, A. Agarwal, et al. <br />
-    [[link]](https://arxiv.org/abs/1603.04467). arXiv:1603.04467, 2016.
-
-12. **The Pascal Visual Object Classes Challenge – A Retrospective,** <br />
-    Mark Everingham, S. M. Ali Eslami, Luc Van Gool, Christopher K. I. Williams, John
-    Winn, and Andrew Zisserma. <br />
-    [[link]](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/). IJCV, 2014.
-
-13. **The Cityscapes Dataset for Semantic Urban Scene Understanding**<br />
-    Cordts, Marius, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, Bernt Schiele. <br />
-    [[link]](https://www.cityscapes-dataset.com/). In CVPR, 2016.
-
-14. **Deep Residual Learning for Image Recognition**<br />
-    Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. <br />
-    [[link]](https://arxiv.org/abs/1512.03385). In CVPR, 2016.
-
-15. **Progressive Neural Architecture Search**<br />
-    Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy. <br />
-    [[link]](https://arxiv.org/abs/1712.00559). In ECCV, 2018.
-
-16. **Searching for MobileNetV3**<br />
-    Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam. <br />
-    [[link]](https://arxiv.org/abs/1905.02244). In ICCV, 2019.