semvec/pypi-packages huggingface dataset:
yolov5processor 1.2
Wrapper package for yolov5 inference:v3.0
Requires Python: >=3.6

# Yolo v5 Inference Engine
Basically a wrapper for the detection program for ease of use.

## How to install:
pip install yolov5processor

## How to use:
```python
from yolov5processor.infer import ExecuteInference 

model = ExecuteInference(weight="path-to-weight.pt", confidence=0.4, \
            img_size=640, agnostic_nms=False, gpu=False, iou=0.5)

image = cv2.imread("imagepath.jpg")
pred = model.predict(image)
```
## Analyse the Output:

```python
print(pred)

{
  "points": [100, 120, 400, 500],
  "conf": 0.7,
  "class": "apple"
}
```
points key represents the bounding box coordinates in the order x1, y1, x2, y2 respectively.
conf key represents the confidence.
class key represents the object class.

Update:
*  Added compatibility with torch 1.9 and torchvision 0.10
yogendra-fizzbuzz 0.4.0
Simple Fizz Buzz Game
Requires Python: >=3.9,<4.0


# Yogendra/FizzBuzz Module

This is a simple demo module to understand python module publishing system.

This module uses

- Python 3.x
- Poetry
- Pytest

## Getting Started

1. Add library to your project

    ```bash
    pip install yogendra_fizzbuzz
    ```

1. Use in your program. Following is an example in repl:

    ```bash
    >>> from yogendra_fizzbuzz import fizzbuzz_to
    >>> print(fizzbuzz_to(16))
    [ '1', '2', 'Fizz', '4', 'Buzz', 'Fizz', '7', '8', 'Fizz', 'Buzz', '11', 'Fizz', '13', '14', 'FizzBuzz', '16']
    ```

## How to build

1. Clone the project

    ```bash
    git clone https://github.com/yogendra/pypi-yogendra_fizzbuzz.git yogendra_fizzbuzz
    cd yogendra_fizzbuzz
    ```

1. Install dependencies

    ```bash
    poetry install 
    ```

1. Build project

    ```bash
    poetry build
    ```

1. (Optional) Publish to Test PyPI

    ```bash
    poetry publish -r testpypi
    ```

1. Publish to PyPI

    ```bash
    poetry publish
    ```
PyRapidML 1.0.13
An open source and low code machine learning library for quick and robust analysis

## PyRapidML
# Introduction
Are you tired of writing hefty lines of code for your data science problem?
Are you having difficulty figuring out what algorithm performs the best?
Is it hard for you to compare multiple algorithms and see which one has the best accuracy?
Do you face issues in Hyperparameter tuning?
Do you want easy model deployments?
Do you a dream of auto-ml?
Are you facing problems in Exploratory data analysis?
Do you want a library that can automatically perform all steps of data science lifecycle?
Do you want a library that can do Exploratory data analysis, Feature Engineering, Feature Selection, Compare multiple Machine Learning Algos, Hyperparamter tuning, model deployments?

If the answer is Yes to the above questions then PyRapidML is the library for you. 

PyRapidML is an open source Python machine learning library.
PyRapidML is essentially a Python wrapper around several machine learning libraries and frameworks such as Pycaret, scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and many more.

PyRapidML is an open source Python library which not only helps in automating Machine Learning Workflows but also helps in building end to end ML algorithms .

PyRapidML is a low code library which means writing basic and less lines of code, one can achieve high accuracy in their machine learning models.
There's no need to write hefty lines of code as PyRapidML would compare all possible machine learning algorithms to solve your problem in just a single line of code.
Once PyRapidML gives you the best algorithm. You can further tune the model (in just a single line of code) to further tune the model.

Initial idea of PyRapidML was inspired by PyCaret library in Python.

# What PyRapidML has to offer currently? What data science problems PyRapidML can cater?
Regression
Classification
Natural Language Processing

# Who is this library for?
This library is for:
Data Scientists
Citizen Data Scientists
Data Science Students
Data Analysts
Data Professionals who want to build end to end data science solutions

# How to install this library?
pip install PyRapidML

# Important Links
- Docs: https://pyrapidml.readthedocs.io/en/latest/ 
- Github Link: https://github.com/Zainali5/PyRapidML 

# Current Release
PyRapidML 1.0.13 is now available. The easiest way to install PyRapidML is using pip.
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