semvec/pypi-packages huggingface dataset:
proyecto-entrega-1 0.1.0
An image classification model for Fashion MNIST using PyTorch Lightning.
License: MIT
Requires Python: >=3.12
Keywords: pytorch, lightning, fashion-mnist, image-classification

## Description

This project implements an image classification model based on the Fashion MNIST dataset, using PyTorch Lightning to structure the code in a modular and scalable way.
The dataset is loaded directly from `torchvision.datasets`. The goal is to train a simple convolutional network that classifies images into 10 different clothing categories.

## Project Structure

models/ # Trained models, checkpoints, and saved weights
src/
 └── my_project/ # Project source code
     ├── dataset.py # Dataset and DataModule
     ├── model.py # PyTorch Lightning model
     ├── train.py # Main training and evaluation script
     └── config.py # Project configurations and parameters
tests/ # Tests for the project
LICENSE # Project license
README.md # Project documentation
pyproject.toml # Dependency management and Poetry configuration
uv.lock # Dependency lock file 

## Installation
To install dependencies and prepare the environment with uv, run the following commands in the terminal:
1. Download and install dependencies: curl -sSf https://uv.io/install.sh | sh
2. Initialize the environment: uv init
3. Sync dependencies and environment: uv sync

## Training and Evaluation
To run the training and evaluation script, first install the package in editable mode:

```bash
uv pip install -e .
```

Then, run the following command in the terminal:

```bash
my_project
```

## Building and Publishing to PyPI
To build and publish the package to PyPI, follow these steps:

1.  **Install build tools:**
    ```bash
    uv pip install build twine
    ```
2.  **Build the package:**
    ```bash
    python -m build
    ```
3.  **Publish to PyPI:**
    ```bash
    twine upload dist/*
    ```
    You will be prompted for your PyPI username and password.


## Technical Details
-Dataset: `torchvision.datasets.FashionMNIST` with custom transformations.
-Model: Simple CNN with one convolutional layer, pooling, and fully connected layers.
-Training: Implemented with PyTorch Lightning to facilitate handling epochs, performance, and metrics.
-Configuration: Parameters such as batch size, paths, epochs defined in config.py.
-Optimization: Adam with CrossEntropyLoss.

## Reports & Visualizations
The project includes detailed reports and visualizations:
- **Confusion matrix**
- **Per-class accuracy**
- **Calibration curve**
- **Misclassified image grids**
- **Pixel distribution analysis**

All outputs are stored in the `reports/` folder.

## Contact
-delrey.132148@e.unavarra.es
-goicoechea.128710@e.unavarra.es
-haddad.179806@e.unavarra.es
vblf 0.2.1
A library to read and write Vector BLF files (binary log format)
License: MIT
Requires Python: >=3.9
Keywords: BLF, Vector, binary log format, Automotive

# Binary Logging Format Library for Python

[![PyPI - Version](https://img.shields.io/pypi/v/vblf.svg)](https://pypi.org/project/vblf)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/vblf.svg)](https://pypi.org/project/vblf)

## Introduction

A Python library for reading and writing BLF files, a proprietary binary logging format of Vector Informatik GmbH. BLF files are commonly used in automotive logging and testing scenarios.

## Installation

```bash
pip install vblf
```

## Usage

### Reading BLF Files

```python
from vblf.can import CanMessage, CanMessage2
from vblf.reader import BlfReader

# Open a BLF file
with BlfReader("example.blf") as reader:
    # Iterate through all objects in the file
    for obj in reader:
        print(f"Type: {obj.header.base.object_type.name}")
        print(f"Timestamp: {obj.header.object_time_stamp}")

        # Handle CAN messages
        if isinstance(obj, (CanMessage, CanMessage2)):
            print(f"CAN ID: {obj.id}")
            print(f"Data: {obj.data.hex()}")
```

### Writing BLF Files

```python
from vblf.can import CanMessage
from vblf.writer import BlfWriter

# Create a new BLF file
with BlfWriter("output.blf") as writer:
    # Create a CAN message
    msg = CanMessage(...)

    # Write the message to the file
    writer.write(msg)
```

## License

This project is licensed under the MIT License.

## Acknowledgments

- Vector Informatik GmbH for the BLF file format
- Tobias Lorenz for his C++ library [vector_blf](https://bitbucket.org/tobylorenz/vector_blf/src/master/)