Fushier's starred repositories
magic_enum
Static reflection for enums (to string, from string, iteration) for modern C++, work with any enum type without any macro or boilerplate code
awesome-chatgpt-prompts
This repo includes ChatGPT prompt curation to use ChatGPT better.
IJCAI2023-OptimalShardedDataParallel
[IJCAI2023] An automated parallel training system that combines the advantages from both data and model parallelism. If you have any interests, please visit/star/fork https://github.com/Youhe-Jiang/OptimalShardedDataParallel
mdistiller
The official implementation of [CVPR2022] Decoupled Knowledge Distillation https://arxiv.org/abs/2203.08679 and [ICCV2023] DOT: A Distillation-Oriented Trainer https://openaccess.thecvf.com/content/ICCV2023/papers/Zhao_DOT_A_Distillation-Oriented_Trainer_ICCV_2023_paper.pdf
awesome_lightweight_networks
The implementation of various lightweight networks by using PyTorch. such as:MobileNetV2,MobileNeXt,GhostNet,ParNet,MobileViT、AdderNet,ShuffleNetV1-V2,LCNet,ConvNeXt,etc. ⭐⭐⭐⭐⭐
TensorRT-for-keras
Optimizing keras models using Nvidia TensorRT
TensorRTkeras
Keras model optimize with TensorRT
KerasToTensorRT
This is a simple demonstration for running Keras model model on Tensorflow with TensorRT integration(TFTRT) or on TensorRT directly without invoking "freeze_graph.py".
LeetCode-Solutions-in-Good-Style
首页已经更新,希望能对大家有帮助。
MIT6.824-Java
Java 实现的分布式系统课程(MIT6.824)
MIT6.824-2021
4 labs + 2 challenges + 4 docs
pytorch-image-models
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
Distributed-Systems
MIT课程《Distributed Systems 》学习和翻译
yolov4_crowdhuman
A tutorial on training a DarkNet YOLOv4 model for the CrowdHuman dataset
tensorrt_demos
TensorRT MODNet, YOLOv4, YOLOv3, SSD, MTCNN, and GoogLeNet
tiny-tensorrt
Deploy your model with TensorRT quickly.