#github project - how to loop through a folder containing multiple images and classifying them using Keras and Pre-trained Networks.
#tensorflow #keras #CNN #Neuralnet #INCEPTIONV3 #Machinelearning #DeepLearning
How it helps ?
ImageClassificationDeepLearning
Here I will show how to loop through a folder containing multiple images and classifying them using Keras and Pre-trained Networks.
#TENSORFLOW #KERAS #NN #NEURALNET #INCEPTIONV3 #MACHINELEARNING #DEEPLEARNING
Our brains make vision seem easy. It doesn't take any effort for humans to tell apart a lion and a jaguar, read a sign, or recognize a human's face. But these are actually hard problems to solve with a computer: they only seem easy because our brains are incredibly good at understanding images.
How it helps ?
Let’s say you have a folder wherein Multiple images get uploaded – best example will be like OLX or Quickr which are free Buy & Sell websites. Now, you need to determine if any harmful things ( e.g. Gun etc) are being bought and sold .
SEVERAL PRE-TRAINED NETWORKS :
- VGG16, VGG19, ResNet50, Inception V3, and Xception
State-of-the-art deep learning image classifiers in Keras
Keras ships out-of-the-box with five Convolutional Neural Networks that have been pre-trained on the ImageNet dataset: VGG16 VGG19 ResNet50 Inception V3 Xception
Inception V3
The goal of the inception module is to act as a “multi-level feature extractor” by computing 1×1, 3×3, and 5×5 convolutions within the same module of the network — the output of these filters are then stacked along the channel dimension and before being fed into the next layer in the network. The original incarnation of this architecture was called GoogLeNet, but subsequent manifestations have simply been called Inception vN where N refers to the version number put out by Google.