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Convolutional Neural Networks Are Beautiful

We all take our eyes for granted, we glance at an object for an instant and our brains can identify with ease. However distorted the information may be, we do a pretty good job at it.

Low light, obscured vision, poor eyesight... There are a myriad of situations where conditions are poor but still we manage to understand what an object is. Context helps, but we humans were created with sight in mind.

Computers have a harder time, but modern advances with convolutional neural networks are making this task a reality.

Computers are amazing, the neural networks and maps they create are beautiful.

Why not have an explore?


MapExtrakt makes viewing feature maps a breeze.

Catch a glimpse of how a computer can see.

MapExtrakt Usage


First import / gather your model (this does not have to be a pretrained pytorch model).

import torchvision
model = torchvision.models.vgg19(pretrained=True)

Import MapExtract's Feature Extractor and load in the model

from MapExtrackt import FeatureExtractor
fe = FeatureExtractor(model)

Set image to be analysed - input can be PIL Image, Numpy array or filepath. We are using the path

fe.set_image("pug.jpg")

View Layers

fe.display_from_map(layer_no=1)

Example Output

View Single Cells At a Time

fe.display_from_map(layer_no=2, cell_no=4)

Example Output

Slice the class to get a range of cells (Layer 2 Cells 0-9)

fe[2,0:10]

Example Output

Or Export Layers To Video

fe.write_video(out_size=(1200,800), file_name="output.avi", time_for_layer=60, transition_perc_layer=0.2)
<a href="https://www.youtube.com/watch?v=LZTGIYxczFc&feature=youtu.be" target="_blank"> <img src="https://raw.githubusercontent.com/lewis-morris/mapextrackt/master/examples/youtube.png" alt="MapExtrackt" border="10" /> </a>

More Examples

For LOTS more - view the jupyter notebook.

Examples


Installation

It's as easy as PyPI

pip install mapextrackt

or build from source in terminal

git clone https://github.com/lewis-morris/mapextrackt &&\
cd mapextrackt &&\
pip install -e .

Todo List


Author

Created by me, initially to view the outputs for my own pleasure.

If anyone has any suggestions or requests please send them over I'd be more than happy to consider.

lewis.morris@gmail.com