3 مستودعات
Interfaces for specifying image and annotation paths to support foreground and background instance labeling.
Distinguishing note: Focuses on the runtime usage and path specification for segmentation tasks rather than initial dataset preparation.
Explore 3 awesome GitHub repositories matching data & databases · Segmentation Data Loaders. Refine with filters or upvote what's useful.
This project is a modular research toolkit designed for developing, training, and evaluating deep learning models for object detection, segmentation, and video instance tracking. It provides a flexible training engine that manages complex neural network execution, including distributed training, custom lifecycle hooks, and weight optimization. The framework is built around a hierarchical configuration system that allows users to define architectures, data pipelines, and training hyperparameters through composable, inheritable files. The project distinguishes itself through its highly modular
The project supports configuring panoptic segmentation datasets by specifying image and annotation paths to enable complex foreground and background instance labeling.
This repository is a comprehensive educational program and deep learning framework designed to teach practical deep learning using PyTorch through notebooks and code examples. It serves as a high-level library for building, training, and deploying neural networks, acting as a model training orchestrator that coordinates PyTorch models, optimizers, and loss functions. The project provides specialized toolkits for computer vision, natural language processing, and tabular data preprocessing. It distinguishes itself through advanced training controls such as discriminative learning rates, a two-w
Provides specialized data loaders and wrappers for image and annotation paths in segmentation tasks.
This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks. The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmen
Wraps DICOM data into data loaders configured for segmentation model training.