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.

Image Captioning

Image Captioning

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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