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./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000001.jpg 1
./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000002.jpg 1823
./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000003.jpg 2903
./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000004.jpg 4470
./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000005.jpg 4698
./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000006.jpg 4797
./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000007.jpg 4917
./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000008.jpg 5072
./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000009.jpg 5177
./cache/flic_win/LMDB/LMDB_val_patches/casino-royale-00039871_00000001_000010.jpg 5271

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conv1 5 1
pool1 2 2
conv2 5 1
pool2 2 2
conv3 5 1
conv4 9 1
conv5 1 1
conv6 1 1
conv7 9 1
conv8 13 1

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Caffe + Ubuntu 12.04 64bit + CUDA 6.5 配置说明

本步骤能实现用Intel核芯显卡来进行显示， 用NVIDIA GPU进行计算。

1. 安装开发所需的依赖包

安装开发所需要的一些基本包

sudo apt-get install build-essential
sudo apt-get install vim cmake git
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev

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conda create -n open-mmlab python=3.7 -y
source activate open-mmlab

conda install -c pytorch pytorch torchvision -y
conda install cython -y
git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
./compile.sh
pip install -e .  # "pip install ." for installation mode

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# extract 24 frames per second
ffmpeg -i Peter-Jasko-solo-M-idzomer-2013.mp4 -r 24/1 frames/solo-dance-%04d.png

# generate video from images, -q 0 is the highest quality
ffmpeg -start_number 222 -i %d.png -q 0 dance_skeleton.avi

ffmpeg -framerate 10 -start_number 0 -i %06d.png -q 0 ../h36m_triangulation.mp4

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To remove a submodule you need to:

• Delete the relevant section from the .gitmodules file.
• Stage the .gitmodules changes

  git add .gitmodules
  

• Delete the relevant section from .git/config.
• Run

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var https = require('https'),
    user = process.argv[2],
    opts = parseOpts(process.argv.slice(3))

request('/users/' + user, function (res) {
    if (!res.public_repos) {
        console.log(res.message)
        return
    }
    var pages = Math.ceil(res.public_repos / 100),

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import numpy as np
from tensorboard.backend.event_processing import event_accumulator as ea

import matplotlib as mpl
import matplotlib.pyplot as plt

def plot_tensorflow_log(path):

    # Loading too much data is slow...
    tf_size_guidance = {

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Configuration of AWS for ELEG 5491

AWS settings (with iPython Notebook)

• Most of the steps can be found in this note: Create an iPython HTML Notebook on Amazon's AWS Free Tier from scratch.
• If you are more comfortable with video tutorial, don't miss this YouTube tutorial: iPython notebook on AWS
• Easier way: After setting your AWS, you can easily install python and notebook by running this script

FTP setting

If you want to use an FTP client to transfer data between local machine and AWS server, you may be instrested in this tutorial.

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