Popular GitHub Repositories for Crop Rect Validation

Image augmentation for machine learning experiments.

structured outputs for llms

The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

Evidently is ??an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

? Open-Source Evaluation & Testing for AI & LLM systems

Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

A python tool that uses GPT-4, FFmpeg, and OpenCV to automatically analyze videos, extract the most interesting sections, and crop them for an improved viewing experience.

A high-level machine learning and deep learning library for the PHP language.

Automatically find issues in image datasets and practice data-centric computer vision.

Perception toolkit for sim2real training and validation in Unity

AI-Video-Cropper is a Python-based tool that leverages the power of GPT-4 (OpenAI's language model) to automatically analyze videos, extract the most interesting sections, and crop them for improved viewing experience. This project combines the capabilities of GPT-4, FFmpeg, and OpenCV to automate the process of identifying highlights in videos

A machine learning based website that recommends the best crop to grow, fertilizers to use, and the diseases caught by your crops.

The toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling.

A full pipeline AutoML tool for tabular data

State-of-the art Automated Machine Learning python library for Tabular Data

Up to 45x faster ? Auto generate type-safe validation code for structs based on markers.

Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles

? A curated list of papers & technical articles on AI Quality & Safety

Performing Leaf Image classification for Recognition of Plant Diseases using various types of CNN Architecture, For detection of Diseased Leaf and thus helping the increase in crop yield.