hoya012/AAAI-2020-Paper-StatisticsJupyter Notebook
Statistics and Visualization of acceptance rate, main keyword of AAAI 2020 accepted papers
(3 stars) (2 forks) (0 件の索引済み issue) (0 件のオープンな good first issue)
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hoya012/CVPR-2019-Paper-StatisticsJupyter Notebook
Statistics and Visualization of acceptance rate, main keyword of CVPR 2019 accepted papers for the main Computer Vision conference (CVPR)
(533 stars) (102 forks) (0 件の索引済み issue) (0 件のオープンな good first issue)
hoya012/CVPR-2020-Paper-StatisticsJupyter Notebook
Statistics and Visualization of acceptance rate, main keyword of CVPR 2020 accepted papers for the main Computer Vision conference (CVPR)
(33 stars) (4 forks) (0 件の索引済み issue) (0 件のオープンな good first issue)
hoya012/CVPR-2021-Paper-StatisticsJupyter Notebook
Statistics and Visualization of acceptance rate, main keyword of CVPR 2021 accepted papers for the main Computer Vision conference (CVPR)
(78 stars) (10 forks) (0 件の索引済み issue) (0 件のオープンな good first issue)
hoya012/CVPR-2022-Paper-StatisticsJupyter Notebook
Statistics and Visualization of acceptance rate, main keyword of CVPR 2022 accepted papers for the main Computer Vision conference (CVPR)
(3 stars) (0 forks) (0 件の索引済み issue) (0 件のオープンな good first issue)
hoya012/CVPR-2023-Paper-StatisticsJupyter Notebook
Statistics and Visualization of acceptance rate, main keyword of CVPR 2023 accepted papers for the main Computer Vision conference (CVPR)
(12 stars) (1 fork) (0 件の索引済み issue) (0 件のオープンな good first issue)
hoya012/ICCV-2019-Paper-StatisticsJupyter Notebook
Statistics and Visualization of acceptance rate, main keyword of ICCV 2019 accepted papers for the main Computer Vision conference (ICCV)
(56 stars) (8 forks) (0 件の索引済み issue) (0 件のオープンな good first issue)
Korean translation of machine learning yearning book by Andrew Ng.
(3 stars) (5 forks) (0 件の索引済み issue) (0 件のオープンな good first issue)
hoya012/NeurIPS-2019-Paper-StatisticsJupyter Notebook
Statistics and Visualization of acceptance rate, main keyword of NeurIPS 2019 accepted papers
(9 stars) (3 forks) (0 件の索引済み issue) (0 件のオープンな good first issue)
hoya012/NeurIPS-2020-Paper-StatisticsJupyter Notebook
Statistics and Visualization of acceptance rate, main keyword of NeurIPS 2020 accepted papers
(16 stars) (4 forks) (0 件の索引済み issue) (0 件のオープンな good first issue)
Automatic Mixed Precision Tutorials using pytorch. Based on PyTorch 1.6 Official Features, implement classification codebase using custom dataset.
(90 stars) (7 forks) (0 件の索引済み issue) (0 件のオープンな good first issue)
A curated list of awesome anomaly detection resources
(2,888 stars) (511 forks) (0 件の索引済み issue) (0 件のオープンな good first issue)
carrier of tricks for image classification tutorials using pytorch.
(103 stars) (21 forks) (0 件の索引済み issue) (0 件のオープンな good first issue)
A paper list of object detection using deep learning.
(11,160 stars) (2,818 forks) (0 件の索引済み issue) (0 件のオープンな good first issue)
hoya012/fast-style-transfer-tutorial-pytorchJupyter Notebook
Simple Tutorials & Code Implementation of fast-style-transfer(Perceptual Losses for Real-Time Style Transfer and Super-Resolution, 2016 ECCV) using PyTorch.
(20 stars) (10 forks) (0 件の索引済み issue) (0 件のオープンな good first issue)
hoya012/fixmatchPython
A simple method to perform semi-supervised learning with limited data.
(1 star) (1 fork) (0 件の索引済み issue) (0 件のオープンな good first issue)
hoya012/glue-factoryPython
Training library for local feature detection and matching
(0 stars) (0 forks) (0 件の索引済み issue) (0 件のオープンな good first issue)
(1 star) (3 forks) (0 件の索引済み issue) (0 件のオープンな good first issue)
(11 stars) (5 forks) (0 件の索引済み issue) (0 件のオープンな good first issue)
hoya012/mixup-cifar10Python
mixup: Beyond Empirical Risk Minimization
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