Q1. What is Alexnet?
Q2. What is VGGNet?
Q3. What is VGG16?
Q4. What is ResNet?
At the ILSVRC 2015, so-called Residual Neural Network (ResNet) by the Kaiming He et al introduced the anovel architecture with “skip connections” and features heavy batch normalisation. Such skip connections are also known as the gated units or gated recurrent units and have the strong similarity to recent successful elements applied in RNNs. Thanks to this technique as they were able to train the NN with 152 layers while still having lower complexity than the VGGNet. It achieves the top-5 error rate of 3.57%, which beats human-level performance on this dataset.
Q5. What is HAAR CASCADE?
Haar Cascade: It is the machine learning object detections algorithm used to identify the objects in an image or the video and based on the concept of features proposed by Paul Viola and Michael Jones in their paper "Rapid Object Detection using a Boosted Cascade of Simple Features" in 2001.
It is a machine learning-based approach where the cascade function is trained from the lot of positive and negative images. It is then used to detect the objects in other images.
The algorithm has four stages:
