semvec/pypi-packages huggingface dataset:
bluedot 2.0.0
A zero boiler plate bluetooth remote
License: MIT
Keywords: raspberry,pi,raspberry pi,bluetooth,remote,android

# Blue Dot

[Blue Dot](http://bluedot.readthedocs.io/en/latest/) allows you to control your Raspberry Pi projects wirelessly - it's a Bluetooth remote and zero boiler plate (super simple to use :) Python library.

## Getting started

1. Install

```
sudo pip3 install bluedot
```

2. Get the [Android Blue Dot app](http://play.google.com/store/apps/details?id=com.stuffaboutcode.bluedot) or use the [Python Blue Dot app](http://bluedot.readthedocs.io/en/latest/bluedotpythonapp.html)

3. Pair your Raspberry Pi

4. Write some code

```python
from bluedot import BlueDot
bd = BlueDot()
bd.wait_for_press()
print("You pressed the blue dot!")
```

5. Press the Blue Dot

See the [getting started guide](http://bluedot.readthedocs.io/en/latest/gettingstarted.html) to 'get started'!

## More

Blue Dot is more than just one button. You can create as many buttons as you want and change their appearance to create your own controller.

Every button is also a joystick. You can tell if a button was pressed in the middle, on the top, bottom, left or right. You can easily create a BlueDot controlled Robot.

Why be restricted by such vague positions like top and bottom though: you can get the exact (x, y) position or even the angle and distance from centre where the button was pressed.

Its not all about when the button was pressed either - pressed, released or moved they all work.

A button can be any colour, square, given give or hidden!

You can press it, slide it, swipe it, rotate it - one blue circle can do a lot!

## Even more

The [online documentation](http://bluedot.readthedocs.io/en/latest/) describes how to use Blue Dot and the Python library including recipes and ideas.
torch-soft 0.1.1
A high-level deep learning library build on top of PyTorch for classification problems...
License: MIT
Requires Python: >=3.6

<p align="center">
  <img src="images/logo.png" alt="tensornet" />
  <br />
  <br />
  <img src="https://img.shields.io/badge/version-0.1.1-blue.svg" alt="Version">
  <a href="https://github.com/shan18/TensorNet/blob/master/LICENSE"><img src="https://img.shields.io/apm/l/atomic-design-ui.svg?" alt="MIT License"></a>
  <br />
</p>

torch_soft is a high level implementatio of pytorch for some of claasification implementation with ease...

## features
TensorNet currently supports the following features
- Model architectures
  - ResNet18
  - A custom model called naiveresnet
- Model utilities
  - Loss functions
    - Cross entropy loss
    - nll_loss
  - Optimizers
    - Stochastic Gradient Descent
  - Regularizers
    - L1 regularization
    - L2 regularization
  - LR Schedulers
    - Step LR
    - Reduce LR on Plateau
    - One Cycle Policy
  - LR Range Test
- Model training and validation
- Datasets (data is is returned via data loaders)
  - MNIST
  - CIFAR10
  - TinyImageNet
- Data Augmentation
  - Resize
  - Padding
  - Random Crop
  - Horizontal Flip
  - Vertical Flip
  - Gaussian Blur
  - Random Rotation
  - CutOut
- GradCAM and GradCAM++ (Gradient-weighted Class Activation Map)
- Result Analysis Tools
  - Plotting changes in validation accuracy and loss during model training
  - Displaying correct and incorrect predictions of a trained model
  - Plotting images in a batch for visualization
  - Plotting gradcam outputs

## How to Use

For examples on how to use torch_soft, refer to the [examples](https://github.com/millermuttu/torch_soft/tree/master/examples) directory.

## Dependencies

torch_soft has the following third-party dependencies
- torch
- torchvision
- torchsummary
- tqdm
- matplotlib
- albumentations
- opencv-python