Neevo AI

segmentation_models.pytorch is a Python library providing neural networks for image semantic segmentation based on PyTorch. It includes over 500 pretrained convolutional and transformer-based backbones. The library facilitates the development and deployment of image segmentation models. It is designed for researchers and practitioners in computer vision.

semantic-segmentation-editor is a web-based labeling tool for creating AI training datasets. It supports both 2D images and 3D point clouds. Developed for autonomous driving research, it is built with React, Paper.js, and three.js. The tool is available as a Meteor app.

synthetic-data-kit is a tool for generating high-quality synthetic datasets to fine-tune LLMs. It allows users to generate reasoning traces and QA pairs. The generated data can be saved to a fine-tuning format using a simple CLI. It is designed to unlock task-specific reasoning in Llama-3 family models.

3D-BAT is a 3D Bounding Box Annotation Tool for point cloud and image labeling. It is an open-source toolbox available on GitHub. The tool supports custom data annotation. It is used for labeling 3D data for machine learning and computer vision applications.

ultimateALPR-SDK is an open-source Automatic Number Plate Recognition (ANPR) library. It is designed for CPUs, GPUs, VPUs, and NPUs using deep learning. The SDK supports multiple character sets and operating systems, including Linux, Windows, and Android. It is available on GitHub.

GigaSpeech is a large, open-source dataset for speech recognition. It is designed for training and evaluating speech recognition models. The dataset contains 10,000 hours of transcribed audio. GigaSpeech is suitable for researchers and developers working on speech recognition technologies.