semvec/pypi-packages huggingface dataset:
facemask 0.1.1
Face Mask detection
License: MIT
Keywords: computer vision

## facemask

------------
### Introduction

Face_mask is a fast mask detection classifier that utilizes pytorch

------------

### Installation

Try and start with

```
pip install facemask
```

-------------

### Use

download the pretrained model from [BaiDu](https://pan.baidu.com/s/111neutXya4cwYpqUUYQ7ew), password: fq1s  

```python
import facemask
model = facemask.FaceMaskDetector(modelpath)

model.detect_image_show(image_path)
# or just return the detect results
bboxs = model.model(image_path)             # bbox_coordinate, confidence, label
```
nvsmi 0.4.2
A (user-)friendly wrapper to nvidia-smi
License: MIT
Requires Python: >=3.6,<4.0
Keywords: packaging,dependency,poetry

# nvsmi

A (user-)friendly wrapper to `nvidia-smi`.

It can be used to filter the GPUs based on resource usage (e.g. to choose the least utilized GPU on a multi-GPU system).

## Usage

### CLI

```
nvsmi --help
nvsmi ls --help
nvsmi ps --help
```

### As a library

```
import nvsmi

nvsmi.get_gpus()
nvsmi.get_available_gpus()
nvsmi.get_gpu_processes()
```

## Prerequisites

- An nvidia GPU
- `nvidia-smi`
- Python 2.7 or 3.6+

## Installation

### pipx

The recommended installation method is [pipx](https://github.com/pipxproject/pipx).
More specifically, you can install `nvsmi` for your user with:

``` shell
pipx install nvsmi
```

The above command will create a virtual environment in `~/.local/pipx/venvs/nvsmi` and
add the `nvsmi` executable in `~/.local/bin`.

### pip

Alternatively you can use good old `pip` but this is more fragile than `pipx`:

```
pip install --user nvsmi
```