Sentence examples for image training from inspiring English sources

We have designed ANNOR to use large-scale image training datasets.

In this paper, the dictionary D is pre-learned by the algorithm in [29] with a natural image training set.

Figure 8 depicts the distribution of feature values for positive (face image) and negative (non-face image) training data, where (F_{1}^) feature is evaluated.

If the ability to create mental images is connected to artistic skill one would have to take this into account in the teaching of art and in designing programmes for mental image training.

Table 2 Working object detection performance Index items Value Number of learning image Training: 1500, test: 500 Number of class 2 Learning iteration 15,000 Intersection over union 89.6% Mean average precision 75.5%.

Table 1 Recognition performance of deep learning Index items Value Number of learning image Training: 9000, test: 1000 Number of class 6 Learning iteration 200,000 Pipe* 71.5, 86.6, 94.1% Valve* 71.9, 85.6, 95.7% * The probability for which the true name of the object is included in the Top(k) (k = 1, 2.3) of the output result of the deep learning.

It applies a "neural network" to recognise the images, training the software up with high-resolution versions of pictures so that it develops an understanding of the properties that it is looking for.

With millions of images, training an accurate classifier may take weeks or even years [5, 6].

The methods in this paper are designed to improve the accuracy of a features-based face recognition system when the pose between the input images and training images are different.

Using pictorial images, trained interviewers administered the DCE in March May, 2013, to 814 household heads and/or their spouse(s) in two rural districts.

The three phases are executed on-line for each image, where training is specific of each single image, requiring no prior training, as it is usual in common machine learning-based approaches, mainly supervised.