labeling

We have a script that labels files recursively. Here is the command that executes the label command. cleartool ls -recurse -vob_only -visible -short | \ xargs cleartool mklabel -replace -follow VPCEUM_9.0.0.99 > label.txt 2>&1 When is encounter files with spaces the name such as /directory/d1/my file is here.doc , the command errors off. How do I get ClearCase/unix to accept the space? VonC You have tow approaches with cleartool mklabel : you can use mklabel directly, with the -recurse option. That will label all the files (with or without space) for you. But that can label files that are not

I'm looking for a way that, given an input image and a neural network, it will output a labeled class for each pixel in the image (sky, grass, mountain, person, car etc). I've set up Caffe (the future-branch) and successfully run the FCN-32s Fully Convolutional Semantic Segmentation on PASCAL-Context model. However, I'm unable to produce clear labeled images with it. Images that visualizes my problem: Input image ground truth And my result: This might be some resolution issue. Any idea of where I'm going wrong? It seems like the 32s model is making large strides and thus working at a coarse

The data I'm playing with comes from the internet source listed below nba <- read.csv("http://datasets.flowingdata.com/ppg2008.csv", sep=",") What I want to do, is create a 2D points graph comparing two metrics from this table, with each player representing a dot on the graph. I have the following code: nbaplot <- ggplot(nba, aes(x= MIN, y= PTS, colour="green", label=Name)) + geom_point() This gives me the following: What I want is a label of player's name right next to the dots. I thought the label function in ggplot's aesthetics would do this for me, but it didn't. I also tried text()