A concise but complete implementation of CLIP with various experimental improvements from recent papers
x-clip (wip) A concise but complete implementation of CLIP with various experimental improvements from recent papers Install Usage
Read more
This project uses Template Matching technique for object detecting by detection of template image over base image
This project uses Template Matching technique for object detecting by detection the template image over base image. REQUIREMENTS Python OpenCV pip install opencv-python pip install Tkinter 📝 CODE EXPLANATION Importing Differnt Libraries import cv2 import tkinter as tk from
Read more
Creating a better flamegraph visualization
How do you visualize performance data so you can easily spot bottlenecks? Brendan Gregg’s flamegraphs are a great solution, adopted by a large number of profilers and performance tools. However, even great solutions can be improved. With a few small tweaks, you can make flamegraphs much easier to read. To see what I mean, I’ll start with a default flamegraph, and then make it better step by step. Most of the improvements can be achieved by using the right tool […]
Read more
Hopefully the the next-generation backend server of bgm.tv
基于 python 的新 api server 开发环境 python 版本: 3.8 依赖管理: poetry web 框架: fastapi quick start: git clone https://github.com/bangumi/server bangumi-server cd bangumi-server python -m venv .venv # MUST use python 3.8 source .venv/bin/activate poetry install –remove-untracked pre-commit install 设置 可设置的环境变量 MYSQL_HOST 默认 127.0.0.1 MYSQL_PORT 默认 3306 MYSQL_DB 默认 bangumi MYSQL_USER 无默认值 MYSQL_PASS 无默认值 启动服务器 uvicorn pol.server:app –reload –port 3000 后端环境
Read more
MogFace: Towards a Deeper Appreciation on Face Detection
Introduction In this repo, we propose a promising face detector, termed as MogFace. Our MogFace consists of 3 novel modules, including Ali-AMS, SSE and HCAM. Our MogFace achieves six champions on WIDER FACE. Prepare Environment conda create -n MogFace python=3.6 conda activate MogFace pip install -r requirements.txt cd utils/nms && python setup.py build_ext –inplace && cd ../.. cd
Read more
PyTorch implementation for OCT-GAN Neural ODE-based Conditional Tabular GANs
Code for reproducing the experiments in the paper: Jayoung Kim*, Jinsung Jeon*, Jaehoon Lee, Jihyeon Hyeong, Noseong Park. “FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models.” International World Wide Web Conference (2021).[arxiv] GitHub View Github
Read more
Uni-Fold: Training your own deep protein-folding models
This package provides an implementation of a trainable, Transformer-based deep protein folding model. We modified the open-source code of DeepMind AlphaFold v2.0 and provided code to train the model from scratch. See the reference and the repository of DeepMind AlphaFold v2.0. To train your own Uni-Fold models, please follow the steps below: 1. Install the environment. Run the following code to install the dependencies of Uni-Fold: conda create -n unifold python=3.8.10 -y conda activate unifold ./install_dependencies.sh Uni-Fold has been tested […]
Read more
The Good Old Days. Testing Out A New Module
The Good Old Days. | Testing Out A New Module- Installation Asciimatics supports Python versions 2 & 3. For the precise list of tested versions, refer to pypi https://pypi.python.org/pypi/asciimatics To install asciimatics, simply install with pip as follows: pip install asciimatics This should install all your dependencies for you. If you don’t use pip or it fails to installthem, you can install the dependencies directly using the packages listed in https://github.com/peterbrittain/asciimatics/blob/master/requirements.txt Additionally, Windows users (who aren’t using pip) will need […]
Read more