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Which is best for image classification RGB or grayscale?
Experimental results showed that classification with grayscale images resulted in higher accuracy classification than with RGB images across the different types of classifiers.
What is the difference between RGB and grayscale image?
The RGB colour space You have 256 different shades of red, green and blue (1 byte can store a value from 0 to 255). The red regions have 0 “blue content”. They’re pure red. And, the channels is a grayscale image (because each channel has 1-byte for each pixel).
How many images per class are sufficient for training a CNN?
Also I suggest if you are using TensorFlow ,read more about GOOGLE’s INCEPTION Image Classifier. It is already trained classifier on google’s image database and you can use it for your images, that way requirements for number of images comes down drastically.
Which is better for image classification, CNN or DNN?
Training time: 5.4 hrs, loss: 0.0546, val_loss: 3.2969 As the results suggest, CNN works much better when dealing with images. We have reduced training time by almost 1 hr. Training loss is very optimized but validation loss is still a bit higher which indicates over-fitting.
Which is the best neural network for image recognition?
ResNet50 is a convolutional neural network which has a depth of 50 layers. It was build and trained by Microsoft in 2015 and you can access the model performance results on their paper, titled Deep Residual Learning for Image Recognition. This model is also trained on more than 1 million images from the ImageNet database.
Which is the deep convolutional network for image recognition?
VGG is a convolutional neural network which has a depth of 19 layers. It was build and trained by Karen Simonyan and Andrew Zisserman at the University of Oxford in 2014 and you can access all the information from their paper, Very Deep Convolutional Networks for Large-Scale Image Recognition, which was published in 2015.