![]() Our contribution with this paper is to provide means to reduce the computations in a twofold approach. Processing large amounts of data also take valuable time, not only from the servers, but also from the users that have to wait for exploiting their results. A typical data center with thousands of servers may consume as much energy as 25,000 households (Dayarathna, Wen, & Fan, Citation2016). Nevertheless, computing power comes at a cost, both financially and environmentally (Whitehead, Andrews, Shah, & Maidment, Citation2014). ![]() The EC has launched an initiative earlier this year to develop Copernicus Data and Information Access Services (DIAS) that facilitate access to these data and allow for a scalable computing environment. The need for computing power that can process the amount of available data can only be expected to increase. An increasing number of platforms are being created that address the storage and processing of these data, both from the institutional and the private sector. This wealth of information, combined with a free full and open access policy, provides new opportunities for applications in forestry, agriculture, and climate change monitoring, to name a few. (1) Go to annotation_tool/launch_gui.py file.The Copernicus program of the European Commission (EC) with its Sentinel satellites produces approximately 10 TB of Earth Observation (EO) data per day. ![]() To implement the script, only two things are required to do: However, for those who are interested in trying the current version, we leave a sample script for annotating the CamVid training images. We are currently working on integrating PixelPick annotation tool into VGG Image Annotator (VIA) which offers much better GUI (and degree of freedom in terms of file formats) than our current python-based version. Note that to make training time manageable, we train on the quarter resolution (256x512) of the original Cityscapes images (1024x2048). (The pretrained MobileNetv2 will be loaded automatically.)įor CamVid and Cityscapes, we report the average of 5 different runs and 3 different runs for PASCAL VOC 2012. To train a MobileNetv2-based DeepLabv3+ network, follow the below lines. Train and validateīy default, the current code validates the model every epoch while training. It is worth noting that, if you set downsample variable in args.py (4 by default), it will first downsample train and val images of Cityscapes and store them within. You don't need to change the directory structure. įor Cityscapes, first visit the link and login to download. Then, set the dir_dataset variable in args.py to the directory path which contains the downloaded dataset.įor CamVid, you need to download SegNet-Tutorial codebase as a zip file and use CamVid directory which contains images/annotations for training and test after unzipping it. Our code is based on Python 3.8 and uses the following Python packages.įollow one of the instructions below to download a dataset you are interest in. Our code, models and annotation tool will be made publicly available. We make the following contributions: (i) We investigate the novel semantic segmentation setting in which labels are supplied only at sparse pixel locations, and show that deep neural networks can use a handful of such labels to good effect (ii) We demonstrate how to exploit this phenomena within an active learning framework, termed PixelPick, to radically reduce labelling cost, and propose an efficient “mouse-free” annotation strategy to implement our approach (iii) We conduct extensive experiments to study the influence of annotation diversity under a fixed budget, model pretraining, model capacity and the sampling mechanism for picking pixels in this low annotation regime (iv) We provide comparisons to the existing state of the art in semantic segmentation with active learning, and demonstrate comparable performance with up to two orders of magnitude fewer pixel annotations on the CamVid, Cityscapes and PASCAL VOC 2012 benchmarks (v) Finally, we evaluate the efficiency of our annotation pipeline and its sensitivity to annotator error to demonstrate its practicality. In this work, we show that in order to achieve a good level of segmentation performance, all you need are a few well-chosen pixel labels. PixelPick mouse-free annotation tool (to be updated)Ī central challenge for the task of semantic segmentation is the prohibitive cost of obtaining dense pixel-level annotations to supervise model training.This is an official implementation of the paper "All you need are a few pixels: semantic segmentation with PixelPick."
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