cvPal
cvPal

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cvPal Documentation

Use cvPal's API through comprehensive documentation and guides.

A comprehensive toolkit for computer vision engineers to manage datasets, generate synthetic images, and streamline data preparation for training and evaluation. Our documentation is open-source and available on GitHub.

Features

  • Synthetic Image Generation - Create custom datasets using text prompts
  • Dataset Merging - Seamlessly combine datasets with multiple attributes
  • Label Management - Effortlessly remove or replace specific labels from your dataset
  • Dataset Reporting - Generate comprehensive reports on your dataset
  • Pandas Integration - Group images and labels into DataFrames for advanced manipulation

Installation

You can either use our remote hosted URL or run the server locally. Get your API key from the dashboard.

Python Package

bash
Install cvPal

Install the cvPal Python package for local development and integration.

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pip install cvpal==1.0.2

Install the cvPal Python package for local development and integration.

Example Usage

python
Basic Usage Example

Generate synthetic datasets and preprocess existing ones with cvPal.

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from cvpal.generate import DetectionDataset
from cvpal.preprocessing import ImagesDetection
 
# Generate synthetic data
detection_dataset = DetectionDataset(model="stable-diffusion")
detection_dataset.generate("a cat looking at the camera", num_images=2, labels=["cat"], output_type="yolo", overwrite=False)
 
# Preprocess existing dataset
cp = ImagesDetection()
cp.read_data("/content/Rock-Paper-Scissors-SXSW-14", data_type="txt")
cp.report()

Generate synthetic datasets and preprocess existing ones with cvPal.

Quick Start

Quickstart Guide

Get up and running with cvPal in minutes

Installation

Detailed installation instructions

MCP Server

Coming Soon

Use cvPal through Model Context Protocol