Expalined VGG-16 With Keras on Custom Dataset | Convolutional Neural Network | Deep Learning

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VGG16 – Convolutional Network for Classification and Detection

VGG16 is a convolution neural net (CNN ) architecture which was used to win ILSVR(Imagenet) competition in 2014. VGG16 makes the improvement over AlexNet by replacing large kernel-sized filters (11 and 5 in the first and second convolutional layer, respectively) with multiple 3×3 kernel-sized filters one after another. VGG16 was trained for weeks and was using NVIDIA Titan Black GPU’s.
Most unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1 and always used same padding and maxpool layer of 2x2 filter of stride 2.
VGG-16 is a convolutional neural network that 16 layers deep.
The default input size for VGG16 model is 224 x 224 pixels with 3 channels for RGB image.

VGG was a breakthrough in the world of Convolutional Neural Networks after

LeNet-5 (1998)
AlexNet (2012)
ZFNet(2013)
GoogleNet/Inception(2014).

Learn how to create your own VGG-16 network on custom dataset.
In this video, I will also explain how to use pre trained VGG-16 network.

If you have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer your queries.

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#computervision #VGG-16 #vgg #vggnetwork #vgg16network #AI #ArtificialIntelligence #DeepLearning

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The code & concepts explained so uncomprehensible

Hameddelavar

One of the best tutorials for beginners in cnn thanks so much ...you deserve more views and likes

vikramreddy

thanks a lot, you are the first person that gave me full explanation so easy way

abdunaim

Thanks a lot! I couldn't explain how much your video tutorials have helped me with my computer vision project

besufekadnegussie

VGG19 pr b koi video bna dein please best explanation of video

helpinghandswithnita

Thanks Aarohi. You brilliantly explained everything. Run all the codes successfully with few changes of course :)

prashantp

thank you so much you are the first person that gave me full explanation and helped to start learning in an easy way you i wish the best for you

ayoubammarboudjelal

thankyou soo much this has cleared all my doubts on vgg16, great job

abhisheksaste

Ma'am, thank you so much for all these videos you have made for deep learning. I am an aspiring ML researcher, but I have had a lot of problem finding proper explanation for all the models and frameworks and their implementations. Your videos are a treasure for anyone who wants to understand these concepts.

abhishekbiswas

Than you very much, maam...This tutorial helps me a lot in completing my final-year project :)

asishchowdhury

Thanks a lotttt Aarohi.
You explained it so well and helped in solving all the error. ❤️❤️

rajnigupta

HELLO MAM, I WAS USING THIS CODE TO DETECT MULTICLASSES.BUT IT IS GIVING ME ERROR OF Failed to convert SparseTensor to Tensor.MAM THIS CODE IS ONLY RESTICTED TO BINARY CLASSES??KINDLY RESPOND🙏🙏🙏🙏🙏🙏🙏

simarsandhu

In this no freezing of layers is implemented? That means, whole model is getting trained again? We have just used the architecture?
Can someone help

nehabhullar

do we need define all layers, can we directly import vgg16 architecture?
vgg16 alreday trained with imagenet dataset, so we can test our image?
do we need to train with our own dataset like ? i did not get this point

RAZZKIRAN

please how do i solve this error @Aaroi
Call arguments received by layer 'sequential' (type Sequential):
• inputs=tf.Tensor(shape=(None, 512, 512, 3), dtype=float32)
• training=True
• mask=Non

faizolaberuagba

I need to classify some documents which is of pdf, image, etc. Will this work ?

craftknightpapercraft

mam explain unsupervised ood detection by maximum classifier discrepancy project code

kuthadiarchu

Thank u so much, 4 this wonderful explanation. u did a great job god bless u.

LAHCEN-AGLAGAL

Good afternoon mam... I am getting "No module named future" error.. Please help me...

shilpajuneja

Mam do you have video for multiclass image classification through cnn using keras

divyanshubse

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