![]() ![]() features that identify children or facial hair) or label additional data for testing.īig thanks to generated. Use Keras ImageDataGenerator & flowfromdirectory class. So the size of the CSV files becomes super big. The problem with this approach is that the images are big in size (i need big images) and three channeled (color images). ![]() To enhance the effectiveness of the model, we could try a different neural network architecture, create descriptive features (e.g. My solutions Convert everything into a big CSV file. where |P(male) - P(female)| was smallest.Ĭhildren, indirect face angles and neutral hair cuts seem to confuse the model. To see where our model is struggling, we can also see where the model was most unsure, i.e. It would also appear that this dataset has many females wearing hats. Interesting! I see a lot of similarities in the general outline of males and the general outline of females (look at the hair!). Now let's see what our model classified as female: example, an input pipeline for your image classification task might look like. First, let's see the top 25 our model classified as male: CSV (comma-separated value) file that contains the filename and label. Not great, but a reasonable start considering we're working on a small 2,000 sample dataset. In the end, this model was able to predict sex with about 80% accuracy. To see the surrounding code, including data transformations and training parameters, check out all my work in this notebook. Note: To use it, we'll need to convert the images to grayscale and resize them to a resolution of 64圆4. I don't remember where I found this model, but it's good for small (1000-10000) image datasets. For a simple computer vision task,Įspecially a classification task, I like to start with a simple model that performs well on the MNIST digit dataset There are a lot of ways to choose a model to do training with. You can download the labels.csv here to continue following along. We could manuallyĬreate our labels using the universal data tool,īut since we're dealing with more than 100 images I'm going use the wao.aiĭownloading a csv from wao.ai, we get a CSV full of the labels we'll learn (shown below). To train our algorithm, we'll need to a sex label for each face. This dataset has two sets of fields: images and annotation meta-data. I repackaged all the images we'd use in this zip file so you can skip the resizing It will serve as a good example of how to encode different features into the TFRecord format. Click to download bondibeach.jpg Keras Image Processing API The Keras deep learning library provides utilities for working with image data. Convert *.jpg -resize 256x256 \ \ ! *.jpg Download the image and place it into your current working directory with the filename bondibeach.jpg.
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