Comments (7)
@nok Thanks, I will check it out.
from autowebcompat.
Can I take this up? I would like to work on this.
from autowebcompat.
Sure!
from autowebcompat.
Please be inspired by https://drivendata.github.io/cookiecutter-data-science/#directory-structure 😉
from autowebcompat.
I think we can have project structure similar to this:
├── LICENSE
├── README.md
├── pycache
│ └── ...
├── autowebcompat
│ ├── init.py
│ ├── network.py
│ ├── utils.py
├── data [17950 entries exceeds filelimit, not opening dir]
├── geckodriver.log
├── label_persons
│ └── amit2rockon.csv
├── labels.csv
├── requirements.txt
├── scripts
│ ├── collect.py
│ ├── data_inconsistencies.py
│ ├── generate_labels.py
│ ├── get_dependencies.py
│ ├── label.py
│ ├── pretrain.py
│ └── train.py
├── setup.cfg
├── test-requirements.txt
├── tests
│ └── init.py
├── tools
│ ├── Nightly.app
│ ├── chrome.app
│ ├── chromedriver
│ └── geckodriver
└── webcompatdata-bzlike.json
from autowebcompat.
scripts based files apart from network.py and utils.py shifted to scripts/ directory for now. Once we transform them into OOP style then we can put it back on autowebcompat
.
Please let me know how it can be improved more.
from autowebcompat.
The structure looks good to me. Either with the scripts in the top-level directory or in the scripts
subdirectory.
from autowebcompat.
Related Issues (20)
- Sort labels when saving them
- test_labels should validate all labels files
- test_labels.py is not actually testing the screenshots actually exist HOT 3
- Limit size of full page screenshot HOT 4
- Script to rename images and labels according to new convention
- Implementing Object Segmentation networks for bounding box annotations HOT 1
- Throw a meaningful error in utils.read_labels when labels.csv is empty HOT 2
- Running pretrain.py gives FileNotFoundError. HOT 4
- Try training a neural network using the responses from a DOM-based tool as features
- Try using the responses from a DOM-based tool as additional features
- Create a web-based tool to show predicted differences HOT 2
- Move the labeling tool to be web-based
- Use multiple releases of each browser
- Try using mdn/browser-compat-data to automatically label screenshot pairs
- Investigate training a model to detect regressions in a browser
- When prefilling an issue on webcompat.com, prefill as much as possible
- Add possibility to navigate to websites on demand
- Use Docker to run browsers and collect screenshots
- Train a baseline classifier HOT 12
- Out of memory error while training vgg16 and vgg19 with imagenet weights on Colab
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from autowebcompat.