semvec/pypi-packages huggingface dataset:
novanet 0.1.1
novanet is a package for image segmentation
Requires Python: >=3.10

# NovaNet


NovaNet: An advanced convolutional neural network architecture derived from the traditional U-Net framework, specifically designed to address complex image segmentation tasks. NovaNet enhances the basic U-Net by incorporating several innovative features aimed at improving both accuracy and contextual awareness. Key enhancements include:

* Multi-Scale Feature Fusion: NovaNet intelligently combines features from various network depths before the final output, ensuring that both fine details and broader semantic contexts are captured effectively. This approach allows the network to make more informed predictions, especially beneficial in tasks requiring precise localization and delineation of intricate structures.
* CoordConv Layers: By integrating CoordConv layers, NovaNet gains an innate spatial awareness, allowing it to better understand and process the positional aspects of input data. This is particularly useful in segmentation tasks where the position and layout of the objects are crucial.
* Gated Convolutional Layers: These layers enable dynamic feature selection during the learning process. By applying gates to the convolutions, NovaNet can selectively emphasize or de-emphasize certain features based on their relevance to the task, leading to more robust and adaptable feature representations.
* Attention Gates: These gates are strategically placed within the network to focus the modelâ€™s capacity on salient parts of the input image, thereby improving the quality of feature extraction and subsequent segmentation performance.
* Adaptive Dilation and SE Blocks: NovaNet uses adaptive dilation rates to adjust its receptive field based on the scale of features in the input, complemented by Squeeze-and-Excitation (SE) blocks that recalibrate channel-wise feature responses to boost the representational power of the network.
NovaNet is designed to be highly flexible and efficient, suitable for a wide range of segmentation tasks, including but not limited to medical imaging, satellite image analysis, and scene parsing. Its architecture supports extensive customization to meet specific performance criteria and adapt to various types of image data.

## Architecture

The NovaNet architecture includes clever use of gated convolution operations making it a better performer than UNet++ in terms of performance and architecture simplicity.
![plot](./images/NovaNet.png)

## Results

![plot](./images/samples_1.png)

![plot](./images/samples_2.png)
py-telegram-sender 0.1.3
A simple library for sending messages and files via Telegram
Requires Python: >=3.6

# py-telegram-sender

py-telegram-sender is a simple and convenient Python library for sending messages and files via a Telegram bot.

## Installation
You can install py-telegram-sender using pip:

```
pip install py-telegram-sender
```

## Usage

### Basic Usage

```python
from py_telegram_sender import TelegramSender

# Create a TelegramSender instance
sender = TelegramSender('YOUR_BOT_TOKEN', 'YOUR_CHAT_ID')

# Send a text message
sender.send_msg('Hello, world!')

# Send a file
sender.send_file('Here is a document', '/path/to/your/file.txt')
```

### Using Backward Compatibility Functions

```python
from py_telegram_sender import set_credentials, send_msg, send_file

# Set credentials
set_credentials('YOUR_BOT_TOKEN', 'YOUR_CHAT_ID')

# Send a text message
send_msg('Hello, world!')

# Send a file
send_file('Here is a document', '/path/to/your/file.txt')
```

## Requirements

- Python 3.6+
- requests

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Contributing

We welcome contributions to the project! Please feel free to create an issue or pull request.   

## Contact

If you have any questions or suggestions, please create an issue in this repository.
CANgSend 0.0.1
CANgSend is a Python package designed for sending and managing CAN (Controller Area Network) messages related to acceleration control. It provides a simple yet flexible API for creating and dispatching CAN messages, making it ideal for applications in automotive and industrial systems where CAN communication is essential.
Requires Python: >=3.9

# Example Package

This is a simple example package. You can use
[GitHub-flavored Markdown](https://github.com/gnanesh-16/CANgSend)
to write your content.