semvec/pypi-packages huggingface dataset:
maby 0.1
U-Net for sub-cellular segmentation from Bright field
License: MIT
Requires Python: >=3.7

# Morphology annotation for budding yeasts

## Installation

```sh
pip install maby
```

Once installed, use command `maby-init` to download the pre-trained models and example images. These will be stored in your python environment, so you may need to use special permissions. We recommend using a pythong environment, such as `conda`.

## Training
Once the package is installed, the command `maby-train` will be accessible from the command line.

```sh
maby-train --directory /path/to/image
```

## Evaluating
To evaluate the performance of the trained model on the validation data, use the command  `maby-evaluate`.

It requires a path to a results directory as input.
```sh
maby-evaluate --directory /path/to/directory --checkpoint /path/to/model.pt
```

## Visualizing results with napari
We visualize results with [napari](https://napari.org/index.html) using the command:

```sh
maby-visualize
```

By default, it loads a network to predict the location of the nucleus, as well as an example time-lapse.
Click `Run` to make a prediction on the example. This may take up to a minute, depending on your hardware.

You can choose which model to run the prediction with in the dropdown menu. The `nucleus` and `vacuole` models are pre-trained, but the `custom`  model is loaded with random initial weights.

If you want to predict on a different `.tif` file, you can choose an image and press `Load Image` to load it into the viewer. The bright field data will be automatically normalized between -1 and 1.

If you want to use a different model trained with `maby-train` on your own dataset, locate the checkpoint file and click `Load model`. Note that this will overwrite which ever model is currently loaded, so we suggest that you first switch to the `custom` model in the model picking pane.
fake-agent 0.1.4
Generate fake user agents on the fly
License: MIT
Keywords: user-agent,fake-user-agent,user agent generator

# Fake Agent
```python
from FakeAgent import Fake_Agent

fa = Fake_Agent()
print(fa.random())
```

**Fake-agent**, generate fake browser user agents without a hassle!


## Links 🔗
- [Documentation 📔](https://itz-fork.github.io/Fake-agent)
- [Features 💪](#features-)
- [Installation ⬇️](#install-)
- [Usage 🤔](#usage-)
- [License 👮](#license-)


## Features 💪
- Easy to use
- Works offline
- No need to install bunch of 3rd party packages


## Install ⬇️
- Stable from [pypi](https://pypi.org/project/fake-agent)
    ```
    pip3 install fake-agent
    ```
- Bleeding edge from [github](https://github.com/Itz-fork/Fake-agent)
    ```
    pip3 install -U https://github.com/Itz-fork/Fake-agent/archive/master.zip
    ```


## Usage 🤔
For more methods and detailed explanation, visit [method documentation](https://itz-fork.github.io/Fake-agent/#/methods/README)


**Import and initialize the `Fake_Agent` class**

```python
from FakeAgent import Fake_Agent

fa = Fake_Agent()
```

- `get()` - Get user agents as a list or generator according to the browser that Fake_Agent class initialized with
    - For more info visit - [Fake_Agent.get documentation](https://itz-fork.github.io/Fake-agent/#/methods/get)

- `random()` - Randomly select user agent
    - For more info visit - [Fake_Agent.random documentation](https://itz-fork.github.io/Fake-agent/#/methods/random)


## License 👮
Licensed under [MIT License](LICENSE)