Projects sharing features with EdgeSAM

This is the implementation for PyTroch 0.4.1. - The HRNet OCR version ia available here. - The PyTroch 1.1 version is available here.

Grounded-Segment-Anything is a suite of specialized tools for multimodal visual analysis, text-based segmentation, and generative image editing. It integrates text-to-bounding-box detection and high-precision image segmentation masks to function as a text-based image segmenter and an automated visual labeling tool. The project enables text-driven image editing by identifying objects through natural language to perform inpainting and element replacement. It further extends visual analysis into three dimensions, allowing for 3D human reconstruction and the generation of 3D bounding boxes from t

Pytorch-UNet is a deep learning implementation designed for semantic image segmentation. It provides a framework for training convolutional neural networks to perform pixel-wise classification, transforming input images into detailed prediction masks. The project utilizes a symmetric encoder-decoder architecture that employs skip-connection feature fusion to recover fine-grained boundary details. It includes support for mixed-precision training to reduce memory usage and accelerate processing speeds. The framework covers the end-to-end segmentation pipeline, from model training using custom

Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.

This repository serves as a centralized collection of state-of-the-art deep learning architectures and reference implementations designed for research and application development. It provides a comprehensive toolkit for computer vision and natural language processing, offering pre-built models and training pipelines for tasks ranging from image classification and object detection to complex sequence modeling. The project distinguishes itself by providing a flexible execution harness that manages the entire training lifecycle, including data ingestion and backpropagation. It supports scalable

Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep learning models across a wide range of visual recognition tasks. It provides a unified interface for core operations including object detection, instance segmentation, pose estimation, and image classification. By utilizing a modular architecture, the platform allows users to swap model components to balance inference speed and accuracy requirements for diverse applications. The framework distinguishes itself through its support for real-time processing and flexible deployment. It in

PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)