nvnnghia

#include <chrono>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <assert.h>
#include <opencv2/core.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/opencv.hpp>

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from tqdm import tqdm
import numpy as np
import os 
import warnings

def bb_intersection_over_union(A, B) -> float:
    xA = max(A[0], B[0])
    yA = max(A[1], B[1])
    xB = min(A[2], B[2])

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#include "common.h"
#include "cudaUtility.h"
#include "mathFunctions.h"
#include "pluginImplement.h"
#include "tensorNet.h"
#include "loadImage.h"
#include "imageBuffer.h"
#include <chrono>
#include <thread>
#include <chrono>

nvnnghia

Updated 4/11/2018

Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install CUDA
• Install cuDNN

Table of contents generated with markdown-toc

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git clone https://github.com/Itseez/opencv.git --depth=1
git clone https://github.com/Itseez/opencv_contrib --depth=1

cd opencv
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE \
      -D CMAKE_INSTALL_PREFIX=/usr/local \
      -D WITH_TBB=ON \
      -D BUILD_NEW_PYTHON_SUPPORT=ON \

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# Source:
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_pets.md

# Create data:
python object_detection/dataset_tools/create_pascal_tf_record.py --data=/home/phatv/nghia/mxnet-yolo/data1/VOCdevkit/ --year=VOC2012 --output_path=val.record --set=val

# Train:
python train.py --logtostderr --train_dir=train/ --pipeline_config_path=train/ssd_mobilenet_v11_coco.config

#combine ckpt to inference graph:

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source: http://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html
2.3. Installing cuDNN on Linux
The following steps describe how to build a cuDNN dependent program. 
Choose the installation method that meets your environment needs. 
For example, the tar file installation applies to all Linux platforms. 
The debian installation package applies to Ubuntu 14.04 and 16.04.

In the following sections:
your CUDA directory path is referred to as /usr/local/cuda/
your cuDNN download path is referred to as <cudnnpath>