Pinned Repositories
2017_Pathological_preliminaries
2017 pathological recognition AI challenge preliminaries (Scoring system)
Anime4K
A High-Quality Real Time Upscaler for Anime Video
Awesome-Chinese-LLM
整理开源的中文大语言模型,以规模较小、可私有化部署、训练成本较低的模型为主,包括底座模型,垂直领域微调及应用,数据集与教程等。
bark
🔊 Text-Prompted Generative Audio Model
Chinese-LLaMA-Alpaca
中文LLaMA&Alpaca大语言模型+本地CPU/GPU部署 (Chinese LLaMA & Alpaca LLMs)
cnn-benchmarks
Benchmarks for popular CNN models
coding-interview-university
A complete computer science study plan to become a software engineer.
ColossalAI
Making large AI models cheaper, faster and more accessible
keras_compressor
Model Compression CLI Tool for Keras.
vision
Datasets, Transforms and Models specific to Computer Vision
shatealaboxiaowang's Repositories
shatealaboxiaowang/pytorch-model-summary
pytorch print the model summary methods
shatealaboxiaowang/distiller
Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://nervanasystems.github.io/distiller
shatealaboxiaowang/vision
Datasets, Transforms and Models specific to Computer Vision
shatealaboxiaowang/keras-surgeon
Pruning and other network surgery for trained Keras models.
shatealaboxiaowang/pyFFTW
A pythonic python wrapper around FFTW
shatealaboxiaowang/tensorflow-build
TensorFlow binaries supporting AVX, FMA, SSE
shatealaboxiaowang/keras_compressor
Model Compression CLI Tool for Keras.
shatealaboxiaowang/tensorflow-triplet-loss
Implementation of triplet loss in TensorFlow
shatealaboxiaowang/model_compression
deep learning model compression based on keras
shatealaboxiaowang/SincNet
SincNet is a neural architecture for efficiently processing raw audio samples.
shatealaboxiaowang/Model-Compression-Papers
Papers for deep neural network compression and acceleration
shatealaboxiaowang/cnn-benchmarks
Benchmarks for popular CNN models
shatealaboxiaowang/2017_Pathological_preliminaries
2017 pathological recognition AI challenge preliminaries (Scoring system)
shatealaboxiaowang/zwk_dicom_tm
Eliminate the sensitive information of dicom file (patient name && Hospital name)