Don’t know how to run Tensorflow Object Detection? In this tutorial, I will show you 10 simple steps to run it on your own machine! We will use Tensorflow version 1.8. Are you ready to start detecting objects?
This guide will help you install Tensorflow on GPU enabled host. You will need Nvidia GPU with Compute Capability equal to 3.0 or greater. You can check CC here. If you don’t have such GPU you can omit CUDA and CUDNN installation and just install tensorflow without GPU support.
You can try to use these steps to install Tensorflow on Windows, but…
Instance segmentation, object detection, drivable areas and lane markings — all you can find in Berkley DeepDrive 100K Dataset. It consists of more than 100 000 HD videos recorded at various times, seasons and weather. The dataset includes localization, timestamp and IMU data.
Data were collected in 4 locations which 3 are close to each other (SF, Berkeley and Bay Area), and the last one is New York.
30th April 2018 new version of Open Images Dataset V4 is released. There is also announced a challenge for best object detection results using this dataset.
Here you can see data examples: Open Images Dataset V4
During ECCV 2018 conference there will be a workshop dedicated Open Images Challenge (presented by Vittorio Ferrari, Alina Kuznetsova, Jasper Uijlings, Rodrigo Benenson, Victor Gomes, Matteo Malloci). They will announce challenge results.
The Challenge has two tracks:
4K dashcam videos versus State of The Art object detection deep nets such as YOLO, SSD or Mask RCNN.
I want to share my datasets I use for testing deep neural networks. I have already tested on 4k videos:
I am using original implementation (Darknet by Joseph Redmon) with 4 different trained weights files. These weights are:
Google offers machine learning REST API for image content understanding.
In this post I would like to show how to easily run image recognition in the cloud with a little help of powerful deep learning models. Several models are accessible using one REST API interface. You can upload your image and get specified insights. You can choose from following:
Distributed file system demo using docker containers.
MooseFS is a distributed file system. It spreads data over several physical commodity servers, which are visible to the user as one resource. For standard file operations MooseFS acts like any other Unix-like file system:
Team IMM approach to European Robotics League Emergency 2017
European Robotics League is funded by the European Union’s Horizon 2020 Program. It is continuation of three earlier projects:
ERL Emergency 2017 is a continuation of Eurathlon 2015 robotic…
Semantic segmentation is one of projects in 3rd term of Udacity’s Self-Driving Car Nanodegree program. The goal is to train deep neural network to identify road pixels using part of the KITTI dataset.
This solution uses VGG16 with 3 skip layers. The size of the input image is 576 x 160. Results for 4k video are generated by resizing prediction to 4k. The results are not perfect because of two factors:
For this result I was using only these 3 technics:
Inspired by this work Dat Tran, I prepared my own dataset and trained improved Pix2Pix net to generate Polish youtuber Krzysztof Gonciarz creating show “Zapytaj Beczkę”.
But first let’s give it a try:
In my first approach I just trained the original net from face2face-demo. It worked! But it was only 256 x 256 resolution which was not enough for me, so I decided to increase resolution to 1024 x 1024.
To increase resolution one needs to add layers to encoder and decoder, there is no simpler way to do it. …