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# Classify Real-world Images with Pre-trained Model
MXNet is a flexible and efficient deep learning framework. One of the cool things that a deep learning
algorithm can do is to classify real world images.
In this example we will show how to use a pretrained Inception-BatchNorm network to predict the content of
real world image. The network architecture is described in [1].
The pre-trained Inception-BatchNorm network can be downloaded from [this link](
This model gives the recent state-of-art prediction accuracy on the image net dataset.
## Package Loading
To get started, we load the `mxnet` package first.
In this example, we also need the imager package to load and preprocess the images in R.
## Load the Pretrained Model
Make sure you unzip the pre-trained model in current folder. And we can use the model
loading function to load the model into R.
download.file('', destfile = '')
model <- mx.model.load("Inception/Inception_BN", iteration = 39)
We also need to load in the mean image, which is used for preprocessing using ```mx.nd.load```.
mean.img <- as.array(mx.nd.load("Inception/mean_224.nd")[["mean_img"]])
## Load and Preprocess the Image
Now we are ready to classify a real image. In this example, we simply take the parrots image
from imager package. But you can always change it to other images.
Load and plot the image:
```{r, fig.align='center'}
im <- load.image(system.file("extdata/parrots.png", package = "imager"))
Before feeding the image to the deep net, we need to do some preprocessing
to make the image fit the input requirement of deepnet. The preprocessing
include cropping, and subtraction of the mean.
Because mxnet is deeply integerated with R, we can do all the processing in R function.
The preprocessing function:
preproc.image <- function(im, mean.image) {
# crop the image
shape <- dim(im)
short.edge <- min(shape[1:2])
xx <- floor((shape[1] - short.edge) / 2)
yy <- floor((shape[2] - short.edge) / 2)
croped <- crop.borders(im, xx, yy)
# resize to 224 x 224, needed by input of the model.
resized <- resize(croped, 224, 224)
# convert to array (x, y, channel)
arr <- as.array(resized) * 255
dim(arr) <- c(224, 224, 3)
# subtract the mean
normed <- arr - mean.img
# Reshape to format needed by mxnet (width, height, channel, num)
dim(normed) <- c(224, 224, 3, 1)
We use the defined preprocessing function to get the normalized image.
normed <- preproc.image(im, mean.img)
## Classify the Image
Now we are ready to classify the image! We can use the predict function
to get the probability over classes.
prob <- predict(model, X = normed)
As you can see ```prob``` is a 1 times 1000 array, which gives the probability
over the 1000 image classes of the input.
We can use the ```max.col``` on the transpose of prob. get the class index.
max.idx <- max.col(t(prob))
The index do not make too much sense. So let us see what it really corresponds to.
We can read the names of the classes from the following file.
synsets <- readLines("Inception/synset.txt")
And let us see what it really is
print(paste0("Predicted Top-class: ", synsets[[max.idx]]))
Actually I do not know what does the word mean when I saw it.
So I searched on the web to check it out.. and hmm it does get the right answer :)
## Extract features
Besides the final classification results, we can also extract the internal features.
We need to get feature layer symbol out of internals first. Here we use `global_pool_output`
as an example.
internals = model$symbol$get.internals()
fea_symbol = internals[[match("global_pool_output", internals$outputs)]]
Next, we rebuild a new model using the feature symbol
model2 <- list(symbol = fea_symbol,
arg.params = model$arg.params,
aux.params = model$aux.params)
class(model2) <- "MXFeedForwardModel"
Then we can do the `predict` using the new model to get the internal results.
You need to set `allow.extra.params = TRUE` since some parameters are not used this time.
global_pooling_feature <- predict(model2, X = normed, allow.extra.params = TRUE)
## Reference
[1] Ioffe, Sergey, and Christian Szegedy. "Batch normalization: Accelerating deep network training by reducing internal covariate shift." arXiv preprint arXiv:1502.03167 (2015).