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VGG16 is a variant of VGG model with 16 convolution layers and we have explored the VGG16 architecture in depth.

VGGNet-16 consists of **16 convolutional layers** and is very appealing because of its **very uniform Architecture**. Similar to AlexNet, it has only 3x3 convolutions, but lots of filters. It can be trained on 4 GPUs for 2â€“3 weeks. It is currently the **most preferred choice in the community for extracting features from images**. The weight configuration of the VGGNet is publicly available and has been used in many other applications and challenges as a baseline feature extractor.

However, VGGNet consists of **138 million parameters**, which can be a bit challenging to handle. VGG can be achieved through **transfer Learning**. In which the model is pretrained on a dataset and the parameters are updated for better accuracy and you can use the parameters values.

### 16 layers of VGG16

1.Convolution using 64 filters

2.Convolution using 64 filters + Max pooling

3.Convolution using 128 filters

4. Convolution using 128 filters + Max pooling

5. Convolution using 256 filters

6. Convolution using 256 filters

7. Convolution using 256 filters + Max pooling

8. Convolution using 512 filters

9. Convolution using 512 filters

10. Convolution using 512 filters+Max pooling

11. Convolution using 512 filters

12. Convolution using 512 filters

13. Convolution using 512 filters+Max pooling

14. Fully connected with 4096 nodes

15. Fully connected with 4096 nodes

16. Output layer with Softmax activation with 1000 nodes.