Pinned Repositories
lightweight-neural-architecture-search
This is a collection of our zero-cost NAS and efficient vision applications.
2048_in_Python
A little 2048 game programmed in Python. Learned from shiyanlou.
C-compiler-in-C
it's a C compiler learned from the Internet, and I followed it to rewrite it. It helps me know how a compiler works primarily.
cache-simulator
A cache simulator for lab 3
ICDAR2019_cTDaR
The ICDAR 2019 cTDaR is to evaluate the performance of methods for table detection (TRACK A) and table recognition (TRACK B). For the first track, document images containing one or several tables are provided. For TRACK B two subtracks exist: the first subtrack (B.1) provides the table region. Thus, only the table structure recognition must be performed. The second subtrack (B.2) provides no a-priori information. This means, the table region and table structure detection has to be done.
ourspike
a spike program for running the risc-v program
simhash-py
Simhash and near-duplicate detection (compatible with py>=3.9)
data-juicer
A one-stop data processing system to make data higher-quality, juicier, and more digestible for (multimodal) LLMs! 🍎 🍋 🌽 ➡️ ➡️🍸 🍹 🍷为大模型提供更高质量、更丰富、更易”消化“的数据！
DAMO-YOLO
DAMO-YOLO: a fast and accurate object detection method with some new techs, including NAS backbones, efficient RepGFPN, ZeroHead, AlignedOTA, and distillation enhancement.
HYLcool's Repositories
HYLcool/ourspike
a spike program for running the risc-v program
HYLcool/simhash-py
Simhash and near-duplicate detection (compatible with py>=3.9)
HYLcool/2048_in_Python
A little 2048 game programmed in Python. Learned from shiyanlou.
HYLcool/C-compiler-in-C
it's a C compiler learned from the Internet, and I followed it to rewrite it. It helps me know how a compiler works primarily.
HYLcool/cache-simulator
A cache simulator for lab 3
HYLcool/ChattingDemo
A chatting program demo in Java using naive methods.
HYLcool/hylcool.github.io
personal homepage forked from academicpages.github.io
HYLcool/ICDAR2019_cTDaR
The ICDAR 2019 cTDaR is to evaluate the performance of methods for table detection (TRACK A) and table recognition (TRACK B). For the first track, document images containing one or several tables are provided. For TRACK B two subtracks exist: the first subtrack (B.1) provides the table region. Thus, only the table structure recognition must be performed. The second subtrack (B.2) provides no a-priori information. This means, the table region and table structure detection has to be done.
HYLcool/Steganography
A Steganography program implemented in Java.
HYLcool/data-juicer
A data-centric text processing system to make data higher-quality, juicier, and more digestible for LLMs! 🍎 🍋 🌽 ➡️ ➡️🍸 🍹 🍷为大语言模型提供更高质量、更丰富、更易”消化“的数据！
HYLcool/onnxruntime
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator