Search Results for "computer based training"
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Showing 383 open source projects for "computer based training"
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YOLOv9
Learning What You Want to Learn Using Programmable Gradient Info
...It is especially relevant for researchers and engineers comparing next-generation YOLO architectures or building production computer vision systems. -
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CoreNet
CoreNet: A library for training deep neural networks
...Its distributed runtime manages synchronization, load balancing, and mixed-precision computation to maximize throughput while minimizing communication bottlenecks. CoreNet integrates tightly with Apple’s proprietary ML stack and hardware, serving as the foundation for research in computer vision, language models, and multimodal systems within Apple AI. The framework includes monitoring tools, fault tolerance mechanisms, and efficient checkpointing for massive training runs. -
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MIVisionX
Set of comprehensive computer vision & machine intelligence libraries
MIVisionX toolkit is a set of comprehensive computer vision and machine intelligence libraries, utilities, and applications bundled into a single toolkit. AMD MIVisionX delivers highly optimized open-source implementation of the Khronos OpenVX™ and OpenVX™ Extensions along with Convolution Neural Net Model Compiler & Optimizer supporting ONNX, and Khronos NNEF™ exchange formats. The toolkit allows for rapid prototyping and deployment of optimized computer vision and machine learning... -
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EasyR1
An Efficient, Scalable, Multi-Modality RL Training Framework
EasyR1 is a streamlined training framework for building “R1-style” reasoning models from open-source LLMs with minimal boilerplate. It focuses on the full reasoning stack—data preparation, supervised fine-tuning, preference or outcome-based optimization, and lightweight evaluation—so you can iterate quickly on chain-of-thought–heavy tasks. The project’s philosophy is practicality: sensible defaults, one-command recipes, and compatibility with popular base models let you stand up experiments without wrestling infrastructure. ... -
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PaddleX
PaddlePaddle End-to-End Development Toolkit
...When the model is trained, we need to divide the training set, the validation set and the test set. Therefore, we need to divide the above data. Using the paddlex command, the data set can be randomly divided into 70% training set, 20% validation set and 10% test set. If you use the PaddleX visualization client for model training, the data set division function is integrated in the client, and you do not need to use command division by yourself. -
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Axon
Nx-powered Neural Networks
...Model Creation API – A high-level model creation API which manages model initialization and application. Optimization API – An API for creating and using first-order optimization techniques based on the Optax library. Training API – An API for quickly training models, inspired by PyTorch Ignite. Axon provides abstractions that enable easy integration while maintaining a level of separation between each component. You should be able to use any of the APIs without dependencies on others. By decoupling the APIs, Axon gives you full control over each aspect of creating and training a neural network. ... -
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CO3D (Common Objects in 3D)
Tooling for the Common Objects In 3D dataset
CO3Dv2 (Common Objects in 3D, version 2) is a large-scale 3D computer vision dataset and toolkit from Facebook Research designed for training and evaluating category-level 3D reconstruction methods using real-world data. It builds upon the original CO3Dv1 dataset, expanding both scale and quality—featuring 2× more sequences and 4× more frames, with improved image fidelity, more accurate segmentation masks, and enhanced annotations for object-centric 3D reconstruction. ... -
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Lua
The Lua development repository, as seen by the Lua team
...Lua was born and raised in Tecgraf, formerly the Computer Graphics Technology Group of PUC-Rio. Lua is now housed at LabLua, a laboratory of the Department of Computer Science of PUC-Rio. -
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MuJoCo Playground
An open source library for GPU-accelerated robot learning
...The project includes classic control benchmarks from dm_control, advanced quadruped and bipedal locomotion systems, and dexterous as well as non-prehensile manipulation setups. It also offers optional vision-based training capabilities through integration with Madrona-MJX, allowing researchers to train policies directly from image input on GPUs. MuJoCo Playground supports both the MJX JAX implementation and the Warp physics engine, enabling flexible use across research pipelines. The environments are designed for fast training, compatibility with reinforcement learning libraries, and real-time trajectory visualization using rscope. -
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Petastorm
Petastorm library enables single machine or distributed training
Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code. Petastorm is an open-source data access library developed at Uber ATG. This library enables single machine or distributed training and evaluation of deep learning models directly from datasets in Apache Parquet format. -
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DGL
Python package built to ease deep learning on graph
Build your models with PyTorch, TensorFlow or Apache MXNet. Fast and memory-efficient message passing primitives for training Graph Neural Networks. Scale to giant graphs via multi-GPU acceleration and distributed training infrastructure. DGL empowers a variety of domain-specific projects including DGL-KE for learning large-scale knowledge graph embeddings, DGL-LifeSci for bioinformatics and cheminformatics, and many others. We are keen to bringing graphs closer to deep learning researchers. ... -
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DeltaForce-OBS-Locker
Delta Action OBS Lock Plugin
DeltaForce-OBS-Locker is a research-oriented repository around OBS-based computer vision experiments for the game Delta Force. The project includes desktop and mobile components and discusses image recognition, rendering capture, simulated input, and real-time detection ideas. Its README states that a YOLOv14-based real-time object detection framework was integrated into the desktop side for visual recognition experiments. -
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torchvision
Datasets, transforms and models specific to Computer Vision
The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. We recommend Anaconda as Python package management system. Torchvision currently supports Pillow (default), Pillow-SIMD, which is a much faster drop-in replacement for Pillow with SIMD, if installed will be used as the default. Also, accimage, if installed can be activated by calling torchvision.set_image_backend('accimage'), libpng, which can be installed via conda conda install libpng or any of the package managers for debian-based and RHEL-based Linux distributions, and libjpeg, which can be installed via conda conda install jpeg or any of the package managers for debian-based and RHEL-based Linux distributions. ... -
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TensorRT
C++ library for high performance inference on NVIDIA GPUs
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for deep learning inference applications. TensorRT-based applications perform up to 40X faster than CPU-only platforms during inference. With TensorRT, you can optimize neural network models trained in all major frameworks, calibrate for lower precision with high accuracy, and deploy to hyperscale data centers,... -
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Shepherd
Runtime substrate that turns agent's execution into a Git-like trace
Shepherd is an early-stage Python runtime for building agents and meta-agents around reversible, inspectable execution traces. It records interactions and workspace changes as durable run histories instead of immediately applying an agent's output. Each result can be reviewed, executed separately, selected, merged, released, or discarded. Tasks are declared as Python functions whose signatures and documentation define the agent contract and required resources. Read-only and read-write... -
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DevPod
Codespaces but open-source, client-only and unopinionated
DevPod is a client-only tool to create reproducible developer environments based on a devcontainer.json on any backend. Each developer environment runs in a container and is specified through a devcontainer.json. Through DevPod providers, these environments can be created on any backend, such as the local computer, a Kubernetes cluster, any reachable remote machine, or in a VM in the cloud. You can think of DevPod as the glue that connects your local IDE to a machine that you want to develop. ... -
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Catlab.jl
A framework for applied category theory in the Julia language
Catlab.jl is a framework for applied and computational category theory, written in the Julia language. Catlab provides a programming library and interactive interface for applications of category theory to scientific and engineering fields. It emphasizes monoidal categories due to their wide applicability but can support any categorical structure that is formalizable as a generalized algebraic theory. First and foremost, Catlab provides data structures, algorithms, and serialization for... -
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Avalanche
End-to-End Library for Continual Learning based on PyTorch
Avalanche is an end-to-end Continual Learning library based on Pytorch, born within ContinualAI with the unique goal of providing a shared and collaborative open-source (MIT licensed) codebase for fast prototyping, training and reproducible evaluation of continual learning algorithms. Avalanche can help Continual Learning researchers in several ways. This module maintains a uniform API for data handling: mostly generating a stream of data from one or more datasets. -
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HUSTOJ
Popular Open Source Online Judge based on PHP/C++/MySQL/Linux
Popular Open Source Online Judge based on PHP/C++/MySQL/Linux for ACM/ICPC and NOIP training, with easy installation. Open source OJ system. HUSTOJ is free software under the GPL. (Only the original part of the code, which uses components from other open-source projects, please follow the agreement of the original component.) Because the web terminal/database/judgment machine are all packaged in the same image and cannot be extended, it is not recommended to use this image for distributed judgment. ... -
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OpenBLAS
Optimized BLAS library based on GotoBLAS2 1.13 BSD version
...Please refer to tools built under Windows using Cmake the cross-platform, open-source build system. The new build system was developed in collaboration with Kitware Inc. Machine-specific optimized BLAS libraries are available for a variety of computer architectures. These optimized BLAS libraries are provided by the computer vendor or by an independent software vendor. -
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Deep Learning Essay Reading
Read classic and new deep learning papers paragraph by paragraph
...Instead of reading entire dense academic papers, contributors provide structured breakdowns and insights into the most influential research from the past decade, often including explanation highlights and key takeaways. The content spans foundational models, architectures, and training methodologies across computer vision, natural language processing, generative models, and other machine learning domains. These summaries help students, researchers, and engineers stay up to date with breakthroughs in the field without needing to sift through full academic documents. With thousands of stars and forks, this repository has become a widely referenced learning resource for anyone interested in understanding the technical ideas behind major advancements. -
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Agent Executor (AX)
Google's open source distributed agent runtime
...It is designed for situations where a model needs to predict choices from a finite set of alternatives, such as ranking, recommendation, preference modeling, or decision behavior analysis. The project provides JAX-based tools for defining and training choice models with automatic differentiation. It focuses on flexible model construction rather than a single fixed estimator, making it useful for researchers who want to experiment with different utility functions and optimization setups. ax is especially relevant for machine learning and econometrics workflows that need scalable, differentiable approaches to choice modeling. ... -
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Tunix
A JAX-native LLM Post-Training Library
Tunix is a JAX-native library for post-training large language models, bringing supervised fine-tuning, reinforcement learning–based alignment, and knowledge distillation into one coherent toolkit. It embraces JAX’s strengths—functional programming, jit compilation, and effortless multi-device execution—so experiments scale from a single GPU to pods of TPUs with minimal code changes. -
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Kueue
Kubernetes-native Job Queueing
Kueue is a set of APIs and controllers for job queueing. It is a job-level manager that decides when a job should be admitted to start (as in pods can be created) and when it should stop (as in active pods should be deleted). Use Kueue to build a multi-tenant batch service with quotas and a hierarchy for sharing resources among teams in your organization. Based on the available quotas, Kueue decides when jobs should wait and when and where they should run. Kueue works in combination with... -
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DeepCTR
Package of deep-learning based CTR models
DeepCTR is a Easy-to-use,Modular and Extendible package of deep-learning based CTR models along with lots of core components layers which can be used to easily build custom models. You can use any complex model with model.fit(), and model.predict(). Provide tf.keras.Model like interface for quick experiment. Provide tensorflow estimator interface for large scale data and distributed training. It is compatible with both tf 1.x and tf 2.x.