Search Results for "benchmarks" - Page 11
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Showing 440 open source projects for "benchmarks"
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hashmap
A Golang lock-free thread-safe HashMap optimized for fastest access
...Reading from the hash map for numeric key types in a thread-safe way is faster than reading from a standard Golang map in an unsafe way and four times faster than Golang's sync.Map. Technical design decisions have been made based on benchmarks that are stored in an external repository: go-benchmark. The library uses a sorted linked list and a slice as an index into that list. The Get() function contains helper functions that have been inlined manually until the Golang compiler will inline them automatically. It optimizes the slice access by circumventing the Golang size check when reading from the slice. -
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Phoronix Test Suite
Open-Source, Automated Benchmarking
The Phoronix Test Suite is the most comprehensive testing and benchmarking platform available that provides an extensible framework for which new tests can be easily added. The Phoronix Test Suite is designed to effectively carry out both benchmarks in a clean, reproducible, and easy-to-use manner. -
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Crunch PNG
Insane(ly slow but wicked good) PNG image optimization
...This approach leads to a significant file size gain relative to lossless approaches at the expense of a relatively modest decrease in image quality. Continuous benchmark testing is available in our GitHub Actions CI. Please see the benchmarks directory of this repository for details about the benchmarking approach and instructions on how to execute benchmarks locally on the reference images distributed in this repository or with your own image files. -
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Mask2Former
Code release for "Masked-attention Mask Transformer
...This leads to accurate masks with sharp boundaries and strong small-object performance while remaining efficient on high-resolution inputs. The project provides extensive configurations and pretrained models across popular benchmarks like COCO, ADE20K, and Cityscapes. Built on top of Detectron2, it includes training scripts, inference tools, and visualization utilities that make experimentation straightforward. -
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Deep learning time series forecasting
Deep learning PyTorch library for time series forecasting
...Flow Forecast was the first time series framework to feature support for transformer-based models and remains the only true end-to-end deep learning for time series forecasting framework. Currently, Task-TS from CoronaWhy primarily maintains this repository. Pull requests are welcome. Historically, this repository provided open-source benchmarks and codes for flash flood and river flow forecasting. Full transformer (SimpleTransformer in model_dict): The full original transformer with all 8 encoder and decoder blocks. Requires passing the target in at inference. -
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RQ-Transformer
Implementation of RQ Transformer, autoregressive image generation
Implementation of RQ Transformer, which proposes a more efficient way of training multi-dimensional sequences autoregressively. This repository will only contain the transformer for now. You can use this vector quantization library for the residual VQ. This type of axial autoregressive transformer should be compatible with memcodes, proposed in NWT. It would likely also work well with multi-headed VQ. I also think there is something deeper going on, and have generalized this to any number of... -
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Acme
A library of reinforcement learning components and agents
Acme is a framework from DeepMind for building scalable and reproducible reinforcement learning agents. It emphasizes modular components, distributed training, and ease of experimentation. -
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CodeSearchNet
Datasets, tools, and benchmarks for representation learning of code
CodeSearchNet is a large-scale dataset and research benchmark designed to advance the development of systems that retrieve source code using natural language queries. The project was created through collaboration between GitHub and Microsoft Research and aims to support research on semantic code search and program understanding. The dataset contains millions of pairs of source code functions and corresponding documentation comments extracted from open-source repositories. These pairs allow... -
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MoCo v3
PyTorch implementation of MoCo v3
...MoCo v3 introduces improvements for training self-supervised ViTs by combining contrastive learning with transformer-based architectures, achieving strong linear and end-to-end fine-tuning performance on ImageNet benchmarks. The repository supports multi-node distributed training, automatic mixed precision, and linear scaling of learning rates for large-batch regimes. It also includes scripts for self-supervised pretraining, linear classification, and fine-tuning within the DeiT framework. -
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MaskFormer
Per-Pixel Classification is Not All You Need for Semantic Segmentation
...The model achieves strong performance and scalability while simplifying training and evaluation workflows. Its successor, Mask2Former, extends the same meta-architecture to achieve state-of-the-art results across all major segmentation benchmarks. MaskFormer’s modular design, dataset integration, and compatibility with existing Detectron2 models make it an essential research tool. -
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pytorch-fcn
PyTorch Implementation of Fully Convolutional Networks
pytorch-fcn is a PyTorch implementation of Fully Convolutional Networks for semantic image segmentation. It reproduces the influential FCN approach that converts classification networks into dense, pixel-level predictors. The package includes FCN32s, FCN16s, FCN8s, and FCN8s-at-once variants with progressively finer output reconstruction. Training code and a PASCAL VOC example are provided so users can reproduce baseline experiments. The repository reports mean intersection-over-union... -
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MetroBoy
A repository of gate-level simulators and tools for the original Game
...The project is primarily intended for research, hardware preservation, and deep technical exploration rather than everyday gameplay. It also includes extensive testing frameworks to validate behavior against known hardware benchmarks and test ROMs. -
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Fuzzer Test Suite
Set of tests for fuzzing engines
The Fuzzer Test Suite is a collection of real-world, bug-rich targets used to evaluate and compare fuzzers under controlled conditions. Rather than synthetic micro-benchmarks, it packages build scripts, corpora, and known-crash oracles so fuzzer authors can measure time-to-crash, coverage growth, and stability. Each target is configured to integrate with common sanitizers, ensuring memory safety bugs surface with precise diagnostics. The suite standardizes experiment parameters—runtime, seeds, and environment—so results are reproducible and comparable across machines and research groups. ... -
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CalDOM
An agnostic, reactive & minimalist (3kb) JavaScript UI library
...In essence, CalDOM is just a wrapper around the native Node/Element. The overall performance drop is about 0.04x compared to vanilla/pure JavaScript. This is based on averaged unit-level benchmarks in handling single & multiple-element instances: View Benchmark Results against Vanilla JS, jQuery, React JS, Vue & more. -
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SimSiam
PyTorch implementation of SimSiam
...SimSiam learns image representations by maximizing similarity between two augmented views of the same image through a Siamese neural network with a stop-gradient operation, preventing feature collapse. This elegant yet effective design achieves strong results in unsupervised learning benchmarks such as ImageNet without requiring contrastive losses. The repository provides scripts for both unsupervised pre-training and linear evaluation, using a ResNet-50 backbone by default. It is compatible with multi-GPU distributed training and can be fine-tuned or transferred to downstream tasks like object detection following the same setup as MoCo. -
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TimeSformer
The official pytorch implementation of our paper
TimeSformer is a vision transformer architecture for video that extends the standard attention mechanism into spatiotemporal attention. The model alternates attention along spatial and temporal dimensions (or designs variants like divided attention) so that it can capture both appearance and motion cues in video. Because the attention is global across frames, TimeSformer can reason about dependencies across long time spans, not just local neighborhoods. The official implementation in PyTorch... -
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jiant
jiant is an nlp toolkit
Jiant is a multitask NLP framework for fine-tuning transformer-based models on multiple natural language understanding (NLU) tasks. -
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1m-go-tcp-server
Benchmarks for implementation of servers
...The repository includes implementations based on goroutines per connection, single epoll, multiple epoll loops, prefork design, worker pools, and Reactor-style event handling. It also includes client implementations and specialized benchmarks for throughput, latency, I/O-bound workloads, and CPU-bound workloads. The project documents Linux tuning values such as file descriptor limits and TCP settings needed for extreme connection counts. It is most useful for engineers studying high-concurrency networking tradeoffs in Go. -
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git-peek
git repo to local editor instantly
...If you paste a link to a file on GitHub, it will quickly open the file in your local editor. If you pass it a GitHub repository, it fetches a tarball instead of using git and decompresses it while downloading it (streaming). From unscientific benchmarks, this is about 2x faster than cloning. While downloading & decompressing the tarball, it also downloads the specific file linked to (or the README.md) via JSDelivr's CDN. -
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octave-benchmark2
benchmarking framework for octave
overhaul of the previous benchmark package -
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RVM
Ruby enVironment Manager (RVM)
RVM is a command-line tool that allows you to easily install, manage, and work with multiple ruby environments from interpreters to sets of gems. RVM is the acronym of Ruby enVironment Manager. It manages Ruby application environments and enables switching between them. RVM lets you deploy each project with its own completely self-contained and dedicated environment, from the specific version of ruby, all the way down to the precise set of required gems to run your application. Having a... -
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FixRes
Reproduces results of "Fixing the train-test resolution discrepancy"
FixRes is a lightweight yet powerful training methodology for convolutional neural networks (CNNs) that addresses the common train-test resolution discrepancy problem in image classification. Developed by Facebook Research, FixRes improves model generalization by adjusting training and evaluation procedures to better align input resolutions used during different phases. The approach is simple but highly effective, requiring no architectural modifications and working across diverse CNN... -
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Image GPT
Large-scale autoregressive pixel model for image generation by OpenAI
Image-GPT is the official research code and models from OpenAI’s paper Generative Pretraining from Pixels. The project adapts GPT-2 to the image domain, showing that the same transformer architecture can model sequences of pixels without altering its fundamental structure. It provides scripts to download pretrained checkpoints of different model sizes (small, medium, large) trained on large-scale datasets and includes utilities for handling color quantization with a 9-bit palette.... -
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BasicSR
Winning Solution in NTIRE19 Challenges on Video Restoration
...The architecture includes bespoke modules (e.g., Pyramid, Cascading and Deformable alignment and Temporal Spatial Attention fusion) that align information across frames and emphasize important features for restoration, enabling state-of-the-art performance on benchmarks such as the REDS challenge. By fusing spatial and temporal context, EDVR produces significantly improved visual quality in restored videos compared with approaches that treat each frame independently. -
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Fast Elixir
Writing Fast Elixir. Collect Common Elixir idioms
Fast Elixir is a curated collection of performance-focused best practices, idioms, and code snippets for writing efficient Elixir programs. The project documents common patterns in Elixir and compares their relative performance using benchmarks to guide developers toward faster and more memory-friendly code. By showcasing side-by-side examples, it highlights not only which approaches are faster but also why certain functions or techniques should be preferred in different scenarios. The repository is designed as a practical reference for developers who want to optimize Elixir applications without diving into premature micro-optimizations. ...
