semvec/pypi-packages huggingface dataset:
yoloxdetect 0.0.10
It is a demo application of the YOLOX model.
License: MIT
Requires Python: >=3.6
Keywords: machine-learning,deep-learning,pytorch,vision,image-classification,object-detection,yolox,yolov7,yolov6,yolo detector,yolov5

<div align="center">
<h3>
  Yolox-Pip: This is a packaged version of the YOLOX for easy installation and use.
</h3>
<h4>
    <img width="800" alt="teaser" src="doc/fig.png">
</h4>
</div>

## <div align="center">Overview</div>

This repo is a packaged version of the [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) for easy installation and use.
### Installation
```
pip install yoloxdetect
```

### Yolox Inference
```python
from yoloxdetect import YoloxDetector

model = YoloxDetector(
    model_path = "kadirnar/yolox_s-v0.1.1", # or "data/weights/yolox_s.pth"
    config_path = "configs.yolox_s",
    device = "cuda:0",
    hf_model=True,
)
model.classes = None
model.conf = 0.25
model.iou = 0.45
model.show = False
model.save = True

pred = model.predict(image='data/images', img_size=640)
```
### Citation
```bibtex
 @article{yolox2021,
  title={YOLOX: Exceeding YOLO Series in 2021},
  author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
  journal={arXiv preprint arXiv:2107.08430},
  year={2021}
}
```
classr 2.0.0
Use microclassifiers in the cloud for spam detection, sentiment analysis and more.
License: MIT
Requires Python: >=3.6,<4.0

# Classr SDK for Python
Use microclassifiers in the cloud for spam detection, sentiment analysis and more.

![Classr logo](./logo.svg)

## Requirements

- Python 3.6 or newer

## Installation

The Classr SDK for Python can be installed using `pip`:

```sh
pip install classr
```

## Usage

Initiaize your microclassifier by passing its UUID to the `Classr` constructor like so:

```python
from classr import Classr

# Initialize cloud microclassifier.
classifier = Classr('acd78708-850b-4cea-aeaa-23cec50d13b6')
```

Now, call the `classify` or `get_info` functions of `classifier` to make use of it:

```python
# Classify unseen input.
document = input('Enter your input: ')
print(f'Predicted class: {classifier.classify(document)}')

# Print macro F1 score of classifier.
info = classifier.get_info()
print(f'Classifier macro F1 score is: {info.f1_score}')
```

If you'd like to use a self-hosted deployment of the Classr application (i.e. not the default official API), you can
pass a different base URL when constructing your `Classr` object:

```python
from classr import Classr

# Initialize cloud microclassifier.
classifier = Classr('acd78708-850b-4cea-aeaa-23cec50d13b6', 'https://self-hosted-classr.example.com/')
```

## Related Projects

This SDK is for the official [Classr application](https://github.com/lambdacasserole/classr) (but will work with a
self-hosted deployment too, of course).

## License

[MIT](LICENSE) © [lambdacasserole](https://github.com/lambdacasserole).