semvec/pypi-packages huggingface dataset:
vigorvision 2.1.5
Advanced AI vision and object detection framework by Vigor Industries Limited.
Requires Python: >=3.8
Keywords: deep-learning, object-detection, vision, AI, Vigor, torch, computer-vision, Vigor Infotech Limited, Vigor-Vision

# ⚙️ VigorVision

![PyPI](https://img.shields.io/pypi/v/vigorvision?color=brightgreen)
![Python](https://img.shields.io/badge/python-3.8%2B-blue)
![Torch](https://img.shields.io/badge/pytorch-2.0%2B-red)
![License](https://img.shields.io/badge/license-MIT-yellow)
![Build](https://img.shields.io/github/actions/workflow/status/VigorIndustries/vigorvision/tests.yml?branch=main)

> **VigorVision** — an advanced, modular AI vision framework built by **Vigor Industries Limited**, designed for **object detection**, **segmentation**, and **industry-specific visual intelligence**.

---

## 🧭 Table of Contents

1. [Overview](#-overview)
2. [Features](#-features)
3. [Installation](#-installation)
4. [Quick Start](#-quick-start)
5. [Model Architecture](#-model-architecture)
6. [Configuration](#-configuration)
7. [Training](#-training)
8. [Visualization & Logging](#-visualization--logging)
9. [Project Structure](#-project-structure)
10. [Contributing](#-contributing)
11. [License](#-license)
12. [Contact](#-contact)

---

## 🧠 Overview

**VigorVision** provides a fully-featured, modular AI framework for vision applications such as:

- Industrial anomaly detection  
- Object tracking and classification  
- Manufacturing process vision systems  
- Smart quality control and inspection  

It leverages **PyTorch**, **Albumentations**, and **Weights & Biases** for maximum performance, extensibility, and interpretability.

---

## ✨ Features

| Category | Highlights |
|-----------|-------------|
| 🧩 **Architecture** | Modular design with customizable backbone, neck, and detection head |
| 🧠 **Training Engine** | Optimized multi-threaded dataloaders with adaptive batch handling |
| 🧮 **Anchors** | Auto-anchor analysis and smart anchor selection for detection models |
| 📈 **Visualization** | Native support for **TensorBoard** and **Weights & Biases (wandb)** |
| 🔧 **Data Augmentation** | Albumentations-based pipeline for advanced transformations |
| ⚡ **Performance** | FP16 (mixed precision) and multi-GPU training support |
| 🧰 **Utilities** | Built-in tools for dataset validation and metrics computation |

---

## ⚙️ Installation

### ✅ From PyPI
```bash
pip install vigorvision
pip install torch>=2.0.0
pip install torchvision>=0.15.0
pip install albumentations>=1.3.0
pip install opencv-python>=4.8.0
pip install numpy>=1.24.0
pip install tqdm>=4.65.0
pip install pyyaml>=6.0
pip install tensorboard>=2.13.0
pip install wandb>=0.16.0
pip install scikit-learn>=1.3.0
pip install scipy>=1.11.0
pip install matplotlib>=3.8.0
```
collagraph-lsp 0.1.2
LSP server for Collagraph (.cgx) files with ruff integration
Requires Python: >=3.10

# Collagraph LSP Server

A Language Server Protocol (LSP) implementation for [Collagraph](https://github.com/fork-tongue/collagraph) `.cgx` files with integrated [ruff](https://github.com/astral-sh/ruff) linting.

Collagraph is a Python port of Vue.js, supporting single-file components in `.cgx` files. This LSP server provides real-time linting and formatting for Python code within these files.

## Features

- **Linting with ruff**: Uses ruff to lint the Python code within the script tag
- **Formatting with ruff**: Uses ruff to format the python code within the script tag and template expressions
- **Python autocompletion**: Provides intelligent code completion for Python code in `<script>` sections using Jedi with full component context


## Installation

Install from PyPi:

```bash
uv tool install collagraph-lsp
# or with pip
pip install collagraph-lsp
```

### Install from source

```bash
# Clone the repository
git clone https://github.com/fork-tongue/collagraph-lsp.git
cd collagraph-lsp
uv tool install .
```

## Usage

### Running the Server

The LSP server communicates over stdin/stdout. To start it:

```bash
# Using uv
uv run collagraph-lsp

# Or run directly
uv run python -m collagraph_lsp.server
```

### Configuration

The server works with default Ruff settings, although it should pick up on your configuration in your project root.

## Editor Integration

Coming soon:

* Sublime Text
* VS Code
* Zed

### LSP settings

Configure which command to use for ruff:

```json
{
	"ruff_command": "/path/to/ruff"
}
```

## Development

### Running Tests

```bash
# Install (development) dependencies
uv sync

# Run tests
uv run pytest

# Run tests with coverage
uv run pytest --cov=collagraph_lsp

# Lint and format
uv run ruff check --fix
uv run ruff format
```

## Related Projects

- [collagraph](https://github.com/fork-tongue/collagraph) - Python port of Vue.js
- [ruff-cgx](https://github.com/fork-tongue/ruff-cgx) - Linter and formatter for cgx files