Yüz Maskesi Algılama

yüz maskesi algılama

Yüz Maskesi Algılama

Bu çalışmada, maskeli ve maskesiz insanların bulundugu dataset kullanılarak maskeli insanı tanıyan bir model oluşturulmuştur. Model ilk olarak yüzü algılıyor ve böylelikle ROI(Region of interest) bulunuyor. Daha sonra bu yüzde maske olup olmadığını algılıyor.  Sınırlı hesaplama kapasitesine sahip cihazlarda da(Raspberry Pi, Google Coral, NVIDIA Jetson Nano,…) kullanılabilmesi için MobileNet V2 mimarisi kullanılmıştır.

Bu model daha da geliştirilerek okul gibi kapalı kalabalık ortamlarda insanların maske kullanıp kullanmadığı kontrol edilebilir.Hatta maske kulanımıyla ilgili istatistikler de toplanabilir.

Fenics

fenics

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Image Captioning

Image Captioning

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quasi architecto beatae vitae dicta sunt explicabo.

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aspernatur aut odit aut fugit, sed quia consequuntur
magni dolores eos qui ratione voluptatem sequi nesciunt.
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sit amet, consectetur, adipisci velit, sed quia non
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Facial Keypoint Detection

michelle

Facial Keypoint Detection

This project will be all about defining and training a convolutional neural network to perform facial keypoint detection, and using computer vision techniques to transform images of faces.

Facial keypoints (also called facial landmarks) are the small magenta dots shown on each of the faces in the image above. In each training and test image, there is a single face and 68 keypoints, with coordinates (x, y), for that face. These keypoints mark important areas of the face: the eyes, corners of the mouth, the nose, etc. These keypoints are relevant for a variety of tasks, such as face filters, emotion recognition, pose recognition, and so on. Here they are, numbered, and you can see that specific ranges of points match different portions of the face.

facial kepoints
keypoints landmarks

For this task, CNN(Convolutional Neural Network) is used. It is recommended to add multiple convolutional layers and things like dropout layers that may prevent overfitting. It is also recommended to look at literature on keypoint detection, such as this paper, to help you determine the structure of your network.

michelle_detected

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sunglass
added sunglass

Image Classifier

flower

Image Classifier 

Flowers Dataset

Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you’d use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications.

In this project, I trained an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you’d train this classifier, then export it for use in your application. We’ll be using this dataset from Oxford of 102 flower categories, you can see a few examples.

I used the MobileNet pre-trained model from TensorFlow Hub to get the image features. Build and train a new feed-forward classifier using those features. Classification probability of some examples are shown below.

result of model

Identify Customer Segments

identify customer reduction

Identify Customer Segments

In this project, I applied unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that you will use has been provided by Bertelsmann Arvato Analytics, and represents a real-life data science task.

Firstly, I converted data that matches a ‘missing’ or ‘unknown’ value code into a numpy NaN value. For categorical data, I would ordinarily need to encode the levels as dummy variables. I have multi-level categoricals (three or more values) so, I can choose to encode the values using multiple dummy variables (e.g. via OneHotEncoder)

Before we apply dimensionality reduction techniques to the data, we need to perform feature scaling so that the principal component vectors are not influenced by the natural differences in scale for features. For the actual scaling function, a StandardScaler instance is suggested, scaling each feature to mean 0 and standard deviation 1 instead of Normalizer for numeric values.

Then,  I used sklearn’s PCA class to apply principal component analysis on the data, thus finding the vectors of maximal variance in the data. To start, you should not set any parameters (so all components are computed) or set a number of components that is at least half the number of features (so there’s enough features to see the general trend in variability).

pca graph
pca graph

I decided to retain first 35 principal components in other words I decrease dimensions of data to 35 dimensions, since it explains approximately 88% variances. I used sklearn’s KMeans class to perform k-means clustering on the PCA-transformed data. Number of cluster for the data is seleceted as 13 by using elbow method.

cluster results

We can say that all cluster are equally exist except cluster #0 in general population in Germany. If we look customer of Bertelsmann Arvato Analytics, cluster #4 and #12 type people have more portions. Therefore the company should reach cluster #4 and #12 type people in general population of Germany to increase their revenue. Because they are potential customer to the company. The company may use digital marketing to reach cluster #4 and $12 kind of people.