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https://github.com/jayachithra/pspnet-tensorflow
https://github.com/jayachithra/pspnet-tensorflow
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- Host: GitHub
- URL: https://github.com/jayachithra/pspnet-tensorflow
- Owner: jayachithra
- Created: 2020-01-31T08:33:06.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-01-31T08:37:34.000Z (almost 5 years ago)
- Last Synced: 2023-03-03T21:55:10.815Z (almost 2 years ago)
- Language: Python
- Size: 41.5 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## About
A PSPNet([Pyramid Scene Parsing Network](http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhao_Pyramid_Scene_Parsing_CVPR_2017_paper.pdf)) implementation with Tensorflow.
## Set up
+ Prepare for dataset
+ Download Cityscape from [https://www.cityscapes-dataset.com/downloads/](https://www.cityscapes-dataset.com/downloads/)
+ Convert labels to trainIds
reference: [https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/preparation/createTrainIdLabelImgs.py](https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/preparation/createTrainIdLabelImgs.py)
+ Generate filename list
+ Make Cityscape dataset have the following directory
```
+ Cityscape
+ leftImg8bit
+ train
+ val
+ test
+ gtFine
+ train
+ val
+ test
```
+ Config 'CITYSCAPE_DIR' in the cityscape.py
+ python cityscape.py
+ The directory should be as follows after run 'python cityscape.py':
```
+ Cityscape
+ img_test.txt
+ img_train.txt
+ img_val.txt
+ anno_test.txt
+ anno_train.txt
+ anno_val.txt
+ leftImg8bit
+ train
+ val
+ test
+ gtFine
+ train
+ val
+ test
```
+ Download the pretrained model
+ download pretrained resnet101 weight from [http://download.tensorflow.org/models/resnet_v2_101_2017_04_14.tar.gz](http://download.tensorflow.org/models/resnet_v2_101_2017_04_14.tar.gz)
+ download the trained weight from [here](https://pan.baidu.com/s/16xW1Ja_PnGVOiy6F0OHhQw) **if you want to inference and evaluate the model**.
## Exec
+ Train
+ for **train + val** dataset
> python train.py --dataset trainval
+ for **train** dataset
> python train.py
+ Inference [Use your trained model or download checkpoint [here](https://pan.baidu.com/s/16xW1Ja_PnGVOiy6F0OHhQw)]
+ Inference an image in test set randomly
> python predict.py --prediction_on test
+ Inference an image in val set randomly
> python predict.py --prediction_on val
+ Inference an image in train set randomly
> python predict.py --prediction_on train
+ Inference an specified image by file path(**or your own image path**)
> python predict.py --file_path /Volumes/Samsung_T5/datasets/Cityscape/leftImg8bit_trainvaltest/leftImg8bit/test/berlin/berlin_000270_000019_leftImg8bit.png
+ Evaluation [Use your trained model or download checkpoint [here](https://pan.baidu.com/s/16xW1Ja_PnGVOiy6F0OHhQw)]
+ On test set
> python evaluate.py --dataset test
+ On val set
> python evaluate.py --dataset val
## Results
| Desc | Repo(%) | Repo(%) | Paper(%) |
| :---:| :---: | :----: | :----: |
|Train set| train | train+val | train+val |
|mIoU| **73.5** | **74.3** | 78.4 |
#### Prediction images
Pictures in test set.
ZhongGuancun Road in Beijing.
## Tensorboard
+ cd **summary** directory and run the following command
> tensorboard --logdir=./
+ loss
+ learning rate
## Reference
+ [https://github.com/mcordts/cityscapesScripts](https://github.com/mcordts/cityscapesScripts)
+ [https://github.com/tensorflow/models/tree/master/research/deeplab](https://github.com/tensorflow/models/tree/master/research/deeplab)
+ [https://github.com/wutianyiRosun/CGNet](https://github.com/wutianyiRosun/CGNet)