semvec/pypi-packages huggingface dataset:
mlx-tune-sklearn 0.7.19
Machine Learning eXperiments Tuner for Scikit-Learn
Requires Python: >=3.9,<3.12

# MLX TUNE SKLEARN 

This directory contains a port of tune sklearn from the [ray project](https://docs.ray.io/en/latest/tune/index.html) to MLX.

The original code can be found [here](https://github.com/ray-project/tune-sklearn)

The original readme can be found here [README_ORIGINAL.md](README_ORIGINAL.md)
YOLOv7Detector 0.0.5
A simple Wrapper for YOLOv7
Requires Python: >=3.6

An unofficial wrapper of the [yolov7](https://github.com/WongKinYiu/yolov7.git) project.

It is very simple, and has only one function: `calculateDetections()`. 
Currently it only supports images, on CPU.

## Installation

The package is hosted on [pypi](https://pypi.org/project/YOLOv7Detector/), so you can install it with pip:

```bash
pip install YOLOv7Detector
```

## Usage
You must download a model from the [yolov7](https://github.com/WongKinYiu/yolov7?tab=readme-ov-file#performance) project page, 
and place it in the root directory of this project. Then you can use the following code to run inference on an image:
```python
from YOLOv7Detector import Detector as det
from PIL import Image


def main():
    # Initialize the YOLO inference object
    detector = det(weights_path='yolov7.pt', conf_thres=0.7, iou_thres=0.45, img_size=640)

    # Load the image
    image = Image.open('test_images/test_4.jpg')

    download_path = 'test_images/test_4_result.jpg'  # Leave as None if not needed

    dets = detector.calculateDetections(image, view_img=True, download_path=download_path)

    print(dets)


if __name__ == '__main__':
    main()

```

This returns a list of dictionaries, each dictionary is formatted as follows:
```python

{
    'class': 'person', 
    'confidence': 0.966009259223938, 
    'bbox': [31.0, 144.0, 469.0, 653.0]
}
```

where `bbox` is a list of `[x1, y1, x2, y2]` coordinates of the bounding box.
With `view_img=True`, the image with bounding boxes will be displayed as such:

![image](test_images/test_4_result.jpg)