#jupyter_notebook #benchmark_framework #deep_learning #dgl #graph_deep_learning #graph_neural_networks #graph_representation_learning #pytorch
https://github.com/graphdeeplearning/benchmarking-gnns
https://github.com/graphdeeplearning/benchmarking-gnns
GitHub
GitHub - graphdeeplearning/benchmarking-gnns: Repository for benchmarking graph neural networks (JMLR 2023)
Repository for benchmarking graph neural networks (JMLR 2023) - graphdeeplearning/benchmarking-gnns
#python #action_recognition #ava #benchmark #deep_learning #i3d #non_local #openmmlab #posec3d #pytorch #slowfast #spatial_temporal_action_detection #temporal_action_localization #tsm #tsn #video_understanding #x3d
https://github.com/open-mmlab/mmaction2
https://github.com/open-mmlab/mmaction2
GitHub
GitHub - open-mmlab/mmaction2: OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark
OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark - open-mmlab/mmaction2
#python #anomaly_detection #benchmark #data_mining #data_sicence #deep_learning #ensemble_learning #machine_learning #neural_networks #outlier_detection #semi_supervised_learning #supervised_learning #unsupervised_learning
https://github.com/Minqi824/ADBench
https://github.com/Minqi824/ADBench
GitHub
GitHub - Minqi824/ADBench: Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022.
Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022. - Minqi824/ADBench
#python #animal_pose_estimation #benchmark #cpm #crowdpose #deeppose #face_keypoint #freihand #hand_pose_estimation #higher_hrnet #hourglass #hrnet #human_pose #mmpose #mpii #mspn #ochuman #pose_estimation #pytorch #rsn #udp
https://github.com/open-mmlab/mmpose
https://github.com/open-mmlab/mmpose
GitHub
GitHub - open-mmlab/mmpose: OpenMMLab Pose Estimation Toolbox and Benchmark.
OpenMMLab Pose Estimation Toolbox and Benchmark. Contribute to open-mmlab/mmpose development by creating an account on GitHub.
#other #anomaly_detection #anomaly_localization #anomaly_segmentation #benchmark #computer_vision #dataset #deep_learning #defect_detection #industrial_image
https://github.com/M-3LAB/awesome-industrial-anomaly-detection
https://github.com/M-3LAB/awesome-industrial-anomaly-detection
GitHub
GitHub - M-3LAB/awesome-industrial-anomaly-detection: Paper list and datasets for industrial image anomaly/defect detection (updating).…
Paper list and datasets for industrial image anomaly/defect detection (updating). 工业异常/瑕疵检测论文及数据集检索库(持续更新)。 - M-3LAB/awesome-industrial-anomaly-detection
#rust #benchmark #cli #command_line #rust #terminal #tool
Hyperfine is a powerful tool that helps you measure how long different commands or programs take to run. Here’s why it’s useful:
- You can compare the speed of different commands or programs easily.
- It runs multiple tests and gives you detailed statistics, including average, minimum, and maximum times.
- You can prepare the system before each test (e.g., clear disk caches) to get accurate results.
- It supports various output formats like CSV, JSON, and Markdown, making it easy to analyze and share results.
- It works on many operating systems, including Windows, macOS, and Linux.
Overall, hyperfine helps you understand which commands or programs are faster and why, making it a valuable tool for optimizing performance.
https://github.com/sharkdp/hyperfine
Hyperfine is a powerful tool that helps you measure how long different commands or programs take to run. Here’s why it’s useful:
- You can compare the speed of different commands or programs easily.
- It runs multiple tests and gives you detailed statistics, including average, minimum, and maximum times.
- You can prepare the system before each test (e.g., clear disk caches) to get accurate results.
- It supports various output formats like CSV, JSON, and Markdown, making it easy to analyze and share results.
- It works on many operating systems, including Windows, macOS, and Linux.
Overall, hyperfine helps you understand which commands or programs are faster and why, making it a valuable tool for optimizing performance.
https://github.com/sharkdp/hyperfine
GitHub
GitHub - sharkdp/hyperfine: A command-line benchmarking tool
A command-line benchmarking tool. Contribute to sharkdp/hyperfine development by creating an account on GitHub.
#cplusplus #assembly #assembly_language #avx512 #benchmark #coroutines #cpp #cpp_programming #cpp17 #cpp20 #cuda #gcc #google_benchmark #hpc #io_uring #linux_kernel #llvm #ptx #ranges #tutorial #tutorials
This repository helps developers improve their coding skills by showing how to write faster and more efficient code. It includes examples for C++, CUDA, and Assembly, focusing on performance optimization techniques. By using this resource, developers can learn how to avoid common pitfalls like performance bottlenecks and improve their coding habits. It also provides benchmarks to compare different coding methods, helping users choose the best approach for their projects. This can lead to significant speed improvements and better use of computer resources.
https://github.com/ashvardanian/less_slow.cpp
This repository helps developers improve their coding skills by showing how to write faster and more efficient code. It includes examples for C++, CUDA, and Assembly, focusing on performance optimization techniques. By using this resource, developers can learn how to avoid common pitfalls like performance bottlenecks and improve their coding habits. It also provides benchmarks to compare different coding methods, helping users choose the best approach for their projects. This can lead to significant speed improvements and better use of computer resources.
https://github.com/ashvardanian/less_slow.cpp
GitHub
GitHub - ashvardanian/less_slow.cpp: Playing around "Less Slow" coding practices in C++ 20, C, CUDA, PTX, & Assembly, from numerics…
Playing around "Less Slow" coding practices in C++ 20, C, CUDA, PTX, & Assembly, from numerics & SIMD to coroutines, ranges, exception handling, networking and use...