معرفی شرکت ها
dlc2action-0.2b2
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
tba
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | dlc2action-0.2b2 |
| نام | dlc2action |
| نسخه کتابخانه | 0.2b2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | A. Mathis Lab |
| ایمیل نویسنده | alexander@deeplabcut.org |
| آدرس صفحه اصلی | https://github.com/amathislab/DLC2Action |
| آدرس اینترنتی | https://pypi.org/project/dlc2action/ |
| مجوز | - |
[](CONTRIBUTING.md)
<a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
DLC2Action is an action segmentation package that makes running and tracking of machine learning experiments easy.
## Installation
### Via the Python Package Index
You can simply install DLC2Action by typing:
```
pip install "dlc2action==0.2b2"
```
### From Github
You can install DLC2Action for development by running this in your terminal.
```
git clone https://github.com/AlexEMG/DLC2Action
cd DLC2Action
conda create --name DLC2Action python=3.9
conda activate DLC2Action
python -m pip install .
```
## Features
The functionality of DLC2Action includes:
- compiling and updating project-specific configuration files,
- filling in configuration dictionaries automatically whenever possible,
- saving training parameters and results,
- running predictions and hyperparameter searches,
- creating active learning files,
- loading hyperparameter search results in experiments and dumping them into configuration files,
- comparing new experiment parameters with the project history and loading pre-computed features (to save time) and previously
created splits (to enforce consistency) when there is a match,
- filtering and displaying training, prediction and hyperparameter search history,
- plotting training curve comparisons
and more.
## A quick example
You can start a new project, run an experiment, visualize it and use the trained model to make a prediction
in a few lines of code.
```python
from dlc2action.project import Project
# create a new project
project = Project('project_name', data_type='data_type', annotation_type='annotation_type',
data_path='path/to/data/folder', annotation_path='path/to/annotation/folder')
# set important parameters, like the set labels you want to predict
project.update_parameters(...)
# run a training episode
project.run_episode('episode_1')
# plot the results
project.plot_episodes(['episode_1'], metrics=['recall'])
# use the model trained in episode_1 to make a prediction for new data
project.run_prediction('prediction_1', episode_names=['episode_1'], data_path='path/to/new_data/folder')
```
## How to get more information?
Check out the [examples](/examples) or [read the documentation](https://alexemg.github.io/DLC2action/html_docs/dlc2action.html) for a taste of what else you can do.
## Acknowledgments
[Liza Kozlova](https://github.com/elkoz) from the [A. Mathis Group at EPFL](https://www.mathislab.org/) is the main developer of DLC2Action.
We are grateful to many people for feedback, alpha-testing and suggestions, in particular to Andy Bonnetto, Lucas Stoffl, Margaret Lane, Marouane Jaakik, Steffen Schneider and Mackenzie Mathis.
## License:
Note that the software is provided "as is", without warranty of any kind, express or implied. If you use the code or data, please cite us!
## Reference:
Stay tuned for our first publication -- Any feedback on this beta release is welcome at this time. Thanks for using DLC2Action.
نیازمندی
| مقدار | نام |
|---|---|
| >=4.62.3 | tqdm |
| >=1.9 | torch |
| >=1.21.2 | numpy |
| >=1.7.1 | scipy |
| ==1.4.3 | pandas |
| >=3.4.3 | matplotlib |
| >=0.5.3 | editdistance |
| >=2.10.0 | optuna |
| >=3.0.9 | openpyxl |
| >=5.1.0 | plotly |
| ==0.16.12 | ruamel.yaml |
| <=1.2 | p-tqdm |
| >=8.0.3 | click |
| >=7.1.2 | pytest |
| >=3.7.0 | tables |
| >=0.13.1 | torchvision |
| >=6.1.1 | ftfy |
| >=2022.8.17 | regex |
| >=1.1.2 | scikit-learn |
| - | jupyter |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8 | Python |
نحوه نصب
نصب پکیج whl dlc2action-0.2b2:
pip install dlc2action-0.2b2.whl
نصب پکیج tar.gz dlc2action-0.2b2:
pip install dlc2action-0.2b2.tar.gz