Year: 2021

15 Posts

TLSC (Test-time Local Statistics Converter)
Revisiting Global Statistics Aggregation for Improving Image Restoration (消除图像复原中的“misalignment”,性能大幅提升) Paper: Revisiting Global Statistics Aggregation for Improving Image Restoration (AAAI 2022) arXiv:https://arxiv.org/pdf/2112.04491.pdf Code: https://github.com/megvii-research/tlsc Reference: [1] 消除图像复原中的“misalignment”,性能大幅提升 https://mp.weixin.qq.com/s/HRm6wPDBeopsmLtilF5K-A [2] https://xiaoqiangzhou.cn/post/chu_revisiting/ 问题的提出: Specifically, with the increasing size of patches for testing, the performance increases in the case of UNet while it increases…
BoTNet (Bottleneck Transformers)
BoTNet (2021-01): 将 Self-Attention 嵌入 ResNet 文章:Bottleneck Transformers for Visual Recognition 论文: https://arxiv.org/abs/2101.11605 摘要: We present BoTNet, a conceptually simple yet powerful backbone architecture that incorporates self-attention for multiple computer vision tasks including image classification, object detection and instance segmentation. By just replacing the spatial convolutions with global self-attention in…
Python 代码格式化工具
项目地址:https://pypi.org/project/autopep8/ 参考文章:https://www.cnblogs.com/wuyongcong/p/9066531.html Autopep8 autopep8 自动格式化 Python 代码以符合 PEP 8 风格指南。它使用 pycodestyle 工具来确定代码的哪些部分需要被格式化。autopep8 能够修复大多数由 pycodestyle 报告的格式化问题。 安装 pip install autopep8 在命令行使用 autopep8 --in-place --aggressive --aggressive YOUR_PYTHON_FILE.py 在PyCharm 配置使用 配置 打开菜单:File ---> Settings ---> Tools ---> External Tools 窗体左上角有一个 + 号 Name: autopep8 # 或者其他名字 Program: autopep8 # 前提必须先安装 Arguments: --in-place --aggressive…
Cheat Sheet
Unzip a set of "*.tar.gz" file for f in *.tar.gz; do tar -xvf "$f"; done List folders under a path import os list_dirs = [name for name in os.listdir("path") if os.path.isdir(name)]
HDF5 Python API
HDF5 Hierarchical Data Format (HDF) is a set of file formats (HDF4, HDF5) designed to store and organize large amounts of data. Document for HDF5 Python API: https://docs.h5py.org/en/stable/build.html Installation Installation with conda: conda install h5py Installation with pre-built wheels pip install h5py Usage import h5py h5_file_name = "my_data.h5" h5_writer =…
[Reading Notes] Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
Source Paper: [ICCV'2017] https://arxiv.org/abs/1703.06868 Authors: Xun Huang, Serge Belongie Code: https://github.com/xunhuang1995/AdaIN-style Contributions In this paper, the authors present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. Arbitrary style transfer: takes a content image $C$ and an arbitrary style image $S$ as inputs,…
[Reading Notes] Collaborative Distillation for Ultra-Resolution Universal Style Transfer
Source Authors: Huan Wang, Yijun Li, Yuehai Wang, Haoji Hu, Ming-Hsuan YangPaper: [CVPR2020] https://arxiv.org/abs/2003.08436Code: https://github.com/mingsun-tse/collaborative-distillation Contributions It proposes a new knowledge distillation method "Collobrative Distillation" based on the exclusive collaborative relation between the encoder and its decoder. It proposes to restrict the students to learn linear embedding of the teacher's…
NVIDIA Xavier Depolyment: ONNXRuntime and TensorRT
Installation Virtual Environment - archiconda Install the archiconda environment at terminal wget https://github.com/Archiconda/build-tools/releases/download/0.2.3/Archiconda3-0.2.3-Linux-aarch64.sh sh Archiconda3-0.2.3-Linux-aarch64.sh Reference: https://blog.csdn.net/qq_40691868/article/details/114362278?spm=1001.2014.3001.5501 [Not Nesessary] In order to enter the system path environment when enter the terminal. It needs to comment the code "conda activate base" in ".bashrc" # added by Archiconda3 0.2.3 installer # >>>…
Cheat Sheet of Ubuntu
TODO This post contains the solutions to some common bugs occured in deep learning with Ubuntu. It contains: Basic usage of Ubuntu CUDA Ubuntu Add/delete user # add a new user NEW_USER_NAME sudo adduser -m NEW_USER_NAME # set password sudo pass NEW_USER_NAME # delete a user sudo deluser NEW_USER_NAME #…
Cheat Sheet of OpenCV
TODO in progress Image Related TODO Video Related Basic video reading video_file = "path/to/video/file.mp4" cap = cv2.VideoCapture(video_file) fps = cap.get(cv2.CAP_PROP_FPS) length = cap.get(cv2.CAP_PROP_FRAME_COUNT) print("Video File %s ==> fps: %.2f, total: %d frames" % (video_file, fps, length)) video_start = int(0) # start from index 0 video_end = video_start + 50000 #…