nn_conv_shrinking.jpg
This image displays a schematic diagram of a Convolutional Neural Network (CNN) architecture, illustrating how an input image is processed through various layers to produce a final classification. The diagram flows from left to right.

On the far left is a rectangular photograph serving as the input. It depicts a street scene on a sunny day with a blue sky and green trees at the top. A silver car is parked on the right side of the road, and buildings are visible on the left. Above this image, text reads "224 x 224 x 3".

Following the input image is a series of three-dimensional blocks representing different layers of the network. The blocks decrease in height and width while increasing in depth (the third number).

1.  First, there is a stack of white rectangular blocks labeled above as "224 x 224 x 64".
2.  Next are red-outlined blocks labeled "112 x 112 x 128".
3.  Then, a stack of white blocks labeled "56 x 56 x 256".
4.  Followed by red-outlined blocks labeled "28 x 28 x 512".
5.  Then white blocks labeled "14 x 14 x 512".
6.  Next are small red-outlined blocks labeled "7 x 7 x 512".

After this point, the structure changes to long, thin horizontal bars representing fully connected layers:
1.  A blue bar labeled "1 x 1 x 4096".
2.  Another blue bar labeled "1 x 1 x 1000".
3.  Finally, a small orange-brown block at the very end of the chain.

In the bottom right corner, there is a legend explaining the color coding for the blocks:
-   A white square outline corresponds to "convolution+ReLU".
-   A red square outline corresponds to "max pooling".
-   A blue square outline corresponds to "fully connected+ReLU".
-   An orange-brown square outline corresponds to "softmax".

At the very bottom of the image, centered text provides a source link: "Source: http://dx.doi.org/10.52278/2415".
This description was generated automatically. Please feel free to ask questions if you have further questions about the nature of the image or its meaning within the presentation.