Yas CRNN model training - Yet Another Genshin Impact Scanner

Last update: Jan 08, 2023

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Deep Learning yas-train

Overview

Yas-Train

Yet Another Genshin Impact Scanner
又一个原神圣遗物导出器

介绍

该仓库为 Yas 的模型训练程序

使用

假设你会设置基本的pytorch环境。

生成数据集

python main.py gen

训练

python main.py train

在mona/config.py处可以修改数据集大小、epoch等参数

如果要加速大数据集的生成，可以使用pargen.py

如果想使用大数据集验证（又不想大量占用硬盘和内存），可以使用onlineval.py来进行在线验证

反馈

Issue
QQ群：801106595

Owner

wormtql

GitHub Repository

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Yas CRNN model training - Yet Another Genshin Impact Scanner

Related tags

Overview

Yas-Train

介绍

相关资料

使用

反馈

Owner

wormtql

PyTorch-lightning implementation of the ESFW module proposed in our paper Edge-Selective Feature Weaving for Point Cloud Matching

Official repository of "Investigating Tradeoffs in Real-World Video Super-Resolution"

Ensembling Off-the-shelf Models for GAN Training

Code for the paper "Graph Attention Tracking". (CVPR2021)

Official repository with code and data accompanying the NAACL 2021 paper "Hurdles to Progress in Long-form Question Answering" (https://arxiv.org/abs/2103.06332).

Dense Passage Retriever - is a set of tools and models for open domain Q&A task.

Source code for Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning

Implementations for the ICLR-2021 paper: SEED: Self-supervised Distillation For Visual Representation.

Auto White-Balance Correction for Mixed-Illuminant Scenes

Asynchronous Advantage Actor-Critic in PyTorch

Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)

ConvMixer unofficial implementation

Roadmap to becoming a machine learning engineer in 2020

Temporally Coherent GAN SIGGRAPH project.

A tight inclusion function for continuous collision detection

Python Assignments for the Deep Learning lectures by Andrew NG on coursera with complete submission for grading capability.

[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen, Kaixiong Zhou, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

NIMA: Neural IMage Assessment

Submodular Subset Selection for Active Domain Adaptation (ICCV 2021)

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Yas CRNN model training - Yet Another Genshin Impact Scanner

Related tags

Overview

Yas-Train

介绍

相关资料

使用

反馈

Owner

wormtql

PyTorch-lightning implementation of the ESFW module proposed in our paper Edge-Selective Feature Weaving for Point Cloud Matching

Official repository of "Investigating Tradeoffs in Real-World Video Super-Resolution"

Ensembling Off-the-shelf Models for GAN Training

Code for the paper "Graph Attention Tracking". (CVPR2021)

Official repository with code and data accompanying the NAACL 2021 paper "Hurdles to Progress in Long-form Question Answering" (https://arxiv.org/abs/2103.06332).

Dense Passage Retriever - is a set of tools and models for open domain Q&A task.

Source code for Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning

Implementations for the ICLR-2021 paper: SEED: Self-supervised Distillation For Visual Representation.

Auto White-Balance Correction for Mixed-Illuminant Scenes

Asynchronous Advantage Actor-Critic in PyTorch

Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)

ConvMixer unofficial implementation

Roadmap to becoming a machine learning engineer in 2020

Temporally Coherent GAN SIGGRAPH project.

A tight inclusion function for continuous collision detection

Python Assignments for the Deep Learning lectures by Andrew NG on coursera with complete submission for grading capability.

[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

NIMA: Neural IMage Assessment

Submodular Subset Selection for Active Domain Adaptation (ICCV 2021)

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen, Kaixiong Zhou, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang