Convolution Neural Network Models – Categorize your fashion images
With the advancement in the deep machine learning technology, there have been a number of business applications that are dependent on computer vision. In fact, there are several segments of the industry where deep learning techniques and tools are applied for recognition of object to make business procedure faster. The fashion and apparel industry is one among them.
You only have to present the photo of the apparel and train deep learning model to foretell the name of the apparel. You can also repeat this process at a high speed to tag several thousands of fashion images that use apparels with great accuracy and performance.
Classification of Image
Suppose there is a set of images which are labelled within one category and you are asked to predict which category it belongs to. You will use a set of test pictures to determine how accurate the predictions were. The challenges and risks linked with this task are scale variation, viewpoint variation, deformation of image, intra-class variation, illumination conditions, image occlusion of fashion modeland background clutter.
Convolutional Neural Networks
CNNs or Convolutional Neural Networks is one of the most famous neural network models that are used for problem like image classification. CNNs works believing that it is better to understand an image locally. The realistic benefit is that with few parameters, you can improve your time to learn and reduce the total amount of data that you need to train your model.
Instead of a connected network of weights from the pixels, a CNN can have too much of weight to take into account a patch of image. A convolution is a sum of the images’ pixel values as the window moves through the entire image. This process of convolution through the whole image with a weight matrix produces one more image. The process of application of convolution is called convolving.
The best thing about the CNN is that the total number of parameters is not dependent on the size of the actual image. A same CNN can be run on 300X300 image and the total parameters won’t even change for the convolution layer.
Datasets of image classification research tend to be large enough. However, you can use data augmentation to enhance the generalization properties. Rescaled images are randomly cropped with horizontal flipping and random brightness shifts along with RGB color are also used. During test time, multi-crop evaluation is used though they are more expensive and with restrained movement of performance.
The main objective of this random rescaling is to know about the vital features of the objects at different positions and scales. Keras doesn’t implement all the data augmentation techniques but you can definitely implement through the function of ImageDataGenerator modules.
So, as we’ve discussed about the basics of convolutional neural networks for processing fashion images, you now know what it means and how this technology of deep learning is used. You may download MeVero App to find and follow your passion if you have the passion of fashion photography.