kandi X-RAY | CBNA Summary
kandi X-RAY | CBNA Summary
CBNA is a Python library. CBNA has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However CBNA build file is not available. You can download it from GitHub.
Marvin Klingner, Mouadh Ayache, and Tim Fingscheidt.
Marvin Klingner, Mouadh Ayache, and Tim Fingscheidt.
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Quality
Security
License
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CBNA has a low active ecosystem.
It has 3 star(s) with 0 fork(s). There are 2 watchers for this library.
It had no major release in the last 6 months.
CBNA has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of CBNA is current.
Quality
CBNA has no bugs reported.
Security
CBNA has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
CBNA is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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CBNA releases are not available. You will need to build from source code and install.
CBNA has no build file. You will be need to create the build yourself to build the component from source.
Installation instructions, examples and code snippets are available.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of CBNA
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of CBNA
CBNA Key Features
No Key Features are available at this moment for CBNA.
CBNA Examples and Code Snippets
No Code Snippets are available at this moment for CBNA.
Community Discussions
No Community Discussions are available at this moment for CBNA.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install CBNA
To run our code you need to go through the following steps:.
Start a fresh conda environment and install the necessary packages. We have provided an environment.yml with our used environment.
Create a dataset folder where all the used datasets will be stored. Specify the environment variables UBNA_DIR_DATASET and UBNA_DIR_CHECKPOINT, leading to the paths of this dataset folder and the folder where the checkpoints are to be saved. Depending on your operating system, this can be done as follows: Windows: setx UBNA_DIR_DATASET <PATH_TO_DATASET> Linux: export UBNA_DIR_DATASET=<PATH_TO_DATASET>
To download the datasets, you can use the following steps. Note that you do not necessarily need all datasets for all experiments (e.g. for GTA to Cityscapes you only need gta-5 and Cityscapes and Cityscapes sequence datasets), but if you download all datasets mentioned here, you should be able to run all experiments described in the paper: Cityscapes: Create a folder called cityscapes in your dataset folder. Go to https://www.cityscapes-dataset.com/downloads/, log in, download and unpack the following dataset parts into that folder: leftImg8bit_trainvaltest.zip gtFine_trainvaltest.zip Cityscapes (sequences): Create a folder called cityscapes_sequence in your dataset folder. Go to https://www.cityscapes-dataset.com/downloads/, log in, download and unpack the following dataset parts into that folder: leftImg8bit_sequence_trainvaltest.zip GTA-5: Download all parts of the GTA-5 dataset from https://download.visinf.tu-darmstadt.de/data/from_games/. Put all color images in a subfolder images and all labels in a subfolder labels. Rename the main folder to gta5 afterwards. GTA-5 (full split): Create an empty folder called gta5_full_split. It will be used later to store the split information for the GTA-5 dataset.
Download the .json files from the following download links and place them in the dataset folders. Cityscapes: https://drive.google.com/drive/folders/1E9RdGX-uAtrU1p_OLjOrVI4tKBdZwg6W?usp=sharing Cityscapes (sequences): https://drive.google.com/drive/folders/1EKGfzjotMc8_R42nHaMmwIfyyv0W4Q42?usp=sharing GTA-5: https://drive.google.com/drive/folders/1uv4iaOiJ0fbZOHcrTcmFLqje5WdXazPR?usp=sharing GTA-5 (full split): https://drive.google.com/drive/folders/18t4Alb8jhk8Y7BqbxAAK9k8-Yqx1wxBE?usp=sharing The resulting folder structure should look as follows. . ├── cityscapes │ ├── gtFine │ ├── leftImg8bit │ ├── basic_files.json │ ├── parameters.json │ ├── train.json │ ├── validation.json │ └── test.json │ ├── cityscapes_sequence │ ├── leftImg8bit_sequence │ ├── basic_files.json │ ├── parameters.json │ ├── train.json │ ├── validation.json │ └── test.json │ ├── gta5 │ ├── images │ ├── labels │ ├── basic_files.json │ └── parameters.json │ |── gta5_full_split | └── train.json
Start a fresh conda environment and install the necessary packages. We have provided an environment.yml with our used environment.
Create a dataset folder where all the used datasets will be stored. Specify the environment variables UBNA_DIR_DATASET and UBNA_DIR_CHECKPOINT, leading to the paths of this dataset folder and the folder where the checkpoints are to be saved. Depending on your operating system, this can be done as follows: Windows: setx UBNA_DIR_DATASET <PATH_TO_DATASET> Linux: export UBNA_DIR_DATASET=<PATH_TO_DATASET>
To download the datasets, you can use the following steps. Note that you do not necessarily need all datasets for all experiments (e.g. for GTA to Cityscapes you only need gta-5 and Cityscapes and Cityscapes sequence datasets), but if you download all datasets mentioned here, you should be able to run all experiments described in the paper: Cityscapes: Create a folder called cityscapes in your dataset folder. Go to https://www.cityscapes-dataset.com/downloads/, log in, download and unpack the following dataset parts into that folder: leftImg8bit_trainvaltest.zip gtFine_trainvaltest.zip Cityscapes (sequences): Create a folder called cityscapes_sequence in your dataset folder. Go to https://www.cityscapes-dataset.com/downloads/, log in, download and unpack the following dataset parts into that folder: leftImg8bit_sequence_trainvaltest.zip GTA-5: Download all parts of the GTA-5 dataset from https://download.visinf.tu-darmstadt.de/data/from_games/. Put all color images in a subfolder images and all labels in a subfolder labels. Rename the main folder to gta5 afterwards. GTA-5 (full split): Create an empty folder called gta5_full_split. It will be used later to store the split information for the GTA-5 dataset.
Download the .json files from the following download links and place them in the dataset folders. Cityscapes: https://drive.google.com/drive/folders/1E9RdGX-uAtrU1p_OLjOrVI4tKBdZwg6W?usp=sharing Cityscapes (sequences): https://drive.google.com/drive/folders/1EKGfzjotMc8_R42nHaMmwIfyyv0W4Q42?usp=sharing GTA-5: https://drive.google.com/drive/folders/1uv4iaOiJ0fbZOHcrTcmFLqje5WdXazPR?usp=sharing GTA-5 (full split): https://drive.google.com/drive/folders/18t4Alb8jhk8Y7BqbxAAK9k8-Yqx1wxBE?usp=sharing The resulting folder structure should look as follows. . ├── cityscapes │ ├── gtFine │ ├── leftImg8bit │ ├── basic_files.json │ ├── parameters.json │ ├── train.json │ ├── validation.json │ └── test.json │ ├── cityscapes_sequence │ ├── leftImg8bit_sequence │ ├── basic_files.json │ ├── parameters.json │ ├── train.json │ ├── validation.json │ └── test.json │ ├── gta5 │ ├── images │ ├── labels │ ├── basic_files.json │ └── parameters.json │ |── gta5_full_split | └── train.json
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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