To do that, we need to reshape the image from , 32, 32, 1 to , 32, 32 like this:. Well, the code above is done just to make Matplotlib imshow function to work properly to display the image data. Finally we can display what we want. Notice that in the figure below most of the predictions are correct.
Only some of those are classified incorrectly. By the way, I found a page on the internet which shows CIFAR image classification researches along with its accuracy ranks. The papers are available in this page , and luckily those are free to download.
Please lemme know if you can obtain higher accuracy on test data! See you in the next article :. A machine learning enthusiast. Sign in. How to teach machine differentiating images using CNN. Muhammad Ardi Follow. Loading modules and dataset The very first thing to do when we are about to write a code is importing all required modules.
Image preprocessing Before doing anything with the images stored in both X variables, I wanna show you several images in the dataset along with its labels. By the way if we wanna save this model for future use, we can just run the following code: model. Written by Muhammad Ardi Follow. More From Medium. Understand sequence to sequence models in a more intuitive way. Lousy dataset? Kornel Dylski in nexocode. Object Detection using Tensorflow2.
Manoj Kesani in Analytics Vidhya. Michael Sugimura. What is Few-Shot Learning? Exploring multiple dimensions of basic text cleaning techniques in python for beginners. If you have a beefy GPU, feel free to experiment with image sizes to see what impact it has on the accuracy of your model.
This function assigns a label of [1, 0] to images of cats, and [0, 1] to images that are not cats. Encoding our image classes as binary vectors in this way is called one-hot-encoding.
It's necessary because neural networks work by using vectors of numbers, and lots of vector multiplication. They wouldn't know what to do with a text label like " cat " or " hot dog ".
After that, we define a function that walks through our image directories and loads images from each directory.
We're only loading instead of the whole data set because most of us aren't using computers and GPUs that can handle all of the images at once. Don't worry - later on, we'll take a look at how to do additional training runs with more images to improve the accuracy of our model.
For each image, we open it, convert it to grayscale, and then resize it to our desired image size. Converting to grayscale is another way of decreasing the amount of data we have to process.
Reducing image size and converting to grayscale are two techniques that are part of a larger technique known as dimensionality reduction. In this case, converting to grayscale was based on a guess that the shapes and pixel intensities that appear in an image would provide more meaningful information for a cat vs. After processing, each image is added to added to an array. The array is returned after all images have been loaded. Let's walk through what's happening here.
We start by creating an instance of Keras' Sequential class. A sequential model is exactly what it sounds like - a neural network through which data will be passed sequentially, in the order in which the layers were added to the network.
Next, you'll see a pattern - we repeatedly add a Conv2D layer to the network, followed by a MaxPooling2D layer, followed by a BatchNormalization layer. This is a very common pattern when building neural networks to classify images. Although a deep explanation of these layer types is beyond the scope of this code-first introduction, let's briefly take a look at each:. Convolutional layers - represented in this case by Keras' Conv2D class - adjust the weights of each neuron based not only on each data point in the input vector, but also on the surrounding data points.
This makes some intuitive sense; features in an image tend to be defined by lines and edges, which only have meaning when you look at a pixel in relation to the other pixels that are near it. Pooling layers - represented here by Keras' MaxPooling2D layers - reduce the overall computational power required to train and use a model, and help the model generalize to learn about features without depending on those features always being at a certain location within an image.
This is handy when building a cat detector, because ideally we'd like our classifier to recognize a cat no matter where in the image the cat appears. These explanations don't give these layers the full coverage they deserve. If you're interesting in learning more, I recommend this great article. Next, we see a BatchNormalization layer. Batch Normalization is a technique that can dramatically reduce the time required to train a deep neural network.
Rather than trying to explain it in a paragraph, I'll instead refer you to this article that does a great job of explaining batch normalization - complete with Keras sample code. Then, we see a Dropout layer. Dropout layers take a certain percentage of the input data they see and set those values to zero. This seems counterintuitive - after all, we just spent time training this network. Why get rid of some of the data? In practice, dropout layers prevent overfitting, which occurs when a neural network learns its input data a little too well.
When overfitting occurs, a network will be very good at classifying the images has been trained on, but this accuracy doesn't generalize, causing subpar performance when classifying never-before-seen images. The Flatten layer, as its name implies, flattens our previous multi-dimensional layers into a single-dimensional vector. This is done because we finish up by adding several Dense layers, which take a single-dimensional vector as input, and output a single-dimensional vector.
Dense layers are typically used to create traditional, non-convolutional neural networks. For a good discussion of how the different layer types compare, I like this fast. We start by loading our training data. We then split it into two arrays.
First, we pull each image into a NumPy array, and the reshape image in the array match the shape of the data the first layer of our network is expecting. Next, we pull out our one-hot-encoded labels into a NumPy array. Loss functions and optimizers are important parts of deep learning, but to get us up and running quickly, I'm outsourcing the explanation of them to this article. You can also read the Keras documentation on loss functions and optimizers to see what it supports.
Finally, we call model. We pass in the training images and labels we loaded, and feed them to our model in batches of We run our training for 10 epochs which means the set of training images is fed to the neural network 10 times. I'll warn you in advance - a training run of 10 epochs on subset of our training image set will not result in a great image classifier. I chose a small number to start because I want CPU-only users to be able to watch their training complete in a reasonable amount of time.
If you're using a GPU, feel free to increase the number of epochs and the number of images you load at once. Active 1 year, 7 months ago. Viewed 5k times. I found solutions that load images with ImageFolder and after convert images in grayscale, using: transforms. Improve this question. Does this answer your question? Add a comment. Active Oldest Votes. Compose [transforms. Improve this answer. Tanya Jain Tanya Jain 8 8 bronze badges. Pure pytorch solution if ImageFolder isn't appropriate You can roll out your own data loading functionalities and If I were you I wouldn't go fastai route as it's pretty high level and takes away control from you you might not need those functionalities anyway.
In principle, all you have to do is to create something like this below: import pathlib import torch from PIL import Image class ImageDataset torch. Szymon Maszke Szymon Maszke Probably worth pointing out that you can provide a loader argument to ImageFolder to override the default loading behavior. Compose [ transforms. ToPILImage , transforms. This directory contains 20 subdirectories, one for each person, named by userid.
Each of these directories contains several different face images of the same person. You will be interested in the images with the following naming convention:. If you've been looking closely in the image directories, you may notice that some images have a.
As it turns out, 16 of the images taken have glitches due to problems with the camera setup; these are the.
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