semvec/pypi-packages huggingface dataset:
quenn 2.0
Quick Neural Network Experimentation
License: MIT

## **Qu**ick **N**eural **N**etwork **E**xperimentation (QueNN)
### pip install quenn
### Higlights
* A convenient framework to easily setup neural network experiments.
* Minimal configuration to setup a newu experimenton new dataset:
    * Only need to initialize neural network architecture, if needed.
    * Create a python dictionary pointing to data ,ground truth, and mask directory(dataspecs.py).
    * Automatic k-fold cross validation.
    * Automatic logging and model checkpointing.
    * Works an all sort of classification and regression task.
    * GPU enabled metrics like precision, recall, f1, overlap, and confusion matrix with maximum GPU utilization.
    * Ability to combine all dataset with correct dataspecs. Combining dataset and running experiments is hassle free.

### [Link to a full working example](https://github.com/sraashis/quennexample)
Sample usecase as follows:
```python
import argparse

import dataspecs as dspec
from quenn.utils.defaultargs import ap
from quenn.runs import run, pooled_run
from classification import MyTrainer, MyDataset

ap = argparse.ArgumentParser(parents=[ap], add_help=False)

dataspecs = [dspec.AV_WIDE, dspec.VEVIO]
if __name__ == "__main__":
    run(ap, dataspecs, MyTrainer, MyDataset)
    pooled_run(ap, dataspecs, MyTrainer, MyDataset)
```

##### **Training+Validation+Test**
    * $python main.py -p train -nch 3 -e 3 -b 2 -sp True
##### **Only Test**
    * $python main.py -p test -nch 3 -e 3 -b 2 -sp True

## References
**Please cite us if you use this framework(quenn) as follows:**
@misc{qenn,
  author = {Khanal, Aashis},
  title = {Quick Neural Network Experimentation},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  url = {https://github.com/sraashis/quenn}
}
tf-rbdl 2.0.3
Rigid Body Dynamics Library using Tensorflow.
License: MIT
Requires Python: >=3.6.0

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3828061.svg)](https://doi.org/10.5281/zenodo.3828061)

# tf_rbdl
tf_rbdl is a python library that contains some essential rigid body dynamics
algorithms such as forward dynamics and inverse dynamics using Tensorflow. It
uses Lie Group representation and provides kinematic and dynamics quatities of
rigid body systems.

## Examples
### General Usages
- See [examples/general_usage.py](https://github.com/junhyeokahn/tf_rbdl/blob/master/examples/general_usage.py) for details.

## Installation
You can install `tf_rbdl` from PyPI:
```bash
$ pip install tf_rbdl
```
or, you can also install from source:
```bash
$ git clone https://github.com/junhyeokahn/tf_rbdl
$ cd tf_rbdl
$ pip install -e .
```

## Citation
```
@misc{junhyeok_ahn_2020_3828061,
  author       = {Junhyeok Ahn},
  title        = {junhyeokahn/tf\_rbdl: tf\_rbdl-v1.2.0},
  month        = may,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v1.2.0},
  doi          = {10.5281/zenodo.3828061},
  url          = {https://doi.org/10.5281/zenodo.3828061}
}
```

## Todo
- [ ] Write examples
- [ ] Parse urdf

## Acknowledgement
This library is based on the book [Modern
Robotics](http://hades.mech.northwestern.edu/index.php/Modern_Robotics) and the
[code](https://github.com/NxRLab/ModernRobotics).