https://github.com/anastassia-b/neural-algorithm-artistic-style

🎨 Convolutional neural network implementation to generate content-and-style transferred images.
https://github.com/anastassia-b/neural-algorithm-artistic-style

art convolutional-neural-networks deep-learning keras

Last synced: 8 months ago
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🎨 Convolutional neural network implementation to generate content-and-style transferred images.

• Host: GitHub
• URL: https://github.com/anastassia-b/neural-algorithm-artistic-style
• Owner: anastassia-b
• Created: 2018-01-22T05:14:27.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2018-03-14T20:48:25.000Z (over 8 years ago)
• Last Synced: 2025-10-28T00:47:19.190Z (8 months ago)
• Topics: art, convolutional-neural-networks, deep-learning, keras
• Language: Python
• Homepage:
• Size: 11.2 MB
• Stars: 23
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# A Neural Algorithm of Artistic Style

["A Neural Algorithm of Artistic Style"](https://arxiv.org/abs/1508.06576)

 (Gatys, et al. 2015) is the source to this project idea. The implementation of this content-and-style transfer network is a collaboration with [@ruggeri](https://github.com/ruggeri).

## Implementation

The goal of this project is to transfer the style of an artwork to the content of a photograph. We use the VGG recognition network and the paper's clever perspective on understanding the "style" of an artwork (similar to an image's "texture").

## Results

#### 1

![milan-style](/docs/result_milan.jpg)

**Figure 1:** Content is captured from the Duomo di Milano image. Styles from Cézanne and Monet are transferred with some success. I decide to experiment more with hyper-parameters to tune the model.

#### 2

![shrine-style](/docs/result_shrine.jpg)

**Figure 2:** Content: Itsukushima Shrine, Style: Cézanne. Learning rate: 10.0, Epochs: 3000. This takes 25 minutes to train on AWS EC2 instance-- performance is what I want to improve next.

#### 3

![starry-style](/docs/result_starry-night.jpg)

**Figure 3:** Content: Tubingen. Style: Van Gogh. I saved the image after every 100 epochs as the model trained, obtaining the learning process in action!



  



#### 4

![mit-klimt](/docs/result_mit_klimt.jpg)

**Figure 4:** Content: MIT Photograph. Style: Klimt. I played around and increased the preference for content over style up to 25.

## Future Directions

* ["Perceptual Losses for Real-Time Style Transfer and Super-Resolution"](https://arxiv.org/abs/1603.08155) (Johnson, et al. 2016)

* Speed up style transfer by training a network that generates the style transferred images. This will use a deep convolutional generator network (along with batch normalization and residual blocks).

## Reference

VGG16 Summary:

* Total params: 14,714,688

* Trainable params: 0

* Non-trainable params: 14,714,688

|Layer (type) |                Output Shape   |           Param # |

| --- | --- | --- |

|input_1 (InputLayer)  |       (None, 768, 1024, 3)  |    0   |

|block1_conv1 (Conv2D)  |      (None, 768, 1024, 64)  |   1792    |  

|block1_conv2 (Conv2D)    |    (None, 768, 1024, 64)  |   36928     |

|block1_pool (MaxPooling2D)  | (None, 384, 512, 64)    |  0         |

|block2_conv1 (Conv2D)   |     (None, 384, 512, 128)   |  73856     |

|block2_conv2 (Conv2D)   |     (None, 384, 512, 128)   |  147584    |

|block2_pool (MaxPooling2D)  | (None, 192, 256, 128)  |   0        |

|block3_conv1 (Conv2D)    |    (None, 192, 256, 256)  |   295168   |

|block3_conv2 (Conv2D)    |    (None, 192, 256, 256)   |  590080    |

|block3_conv3 (Conv2D)    |    (None, 192, 256, 256)  |   590080    |

|block3_pool (MaxPooling2D) |  (None, 96, 128, 256)  |    0         |

|block4_conv1 (Conv2D)   |     (None, 96, 128, 512)  |    1180160   |

|block4_conv2 (Conv2D)    |    (None, 96, 128, 512)   |   2359808   |

|block4_conv3 (Conv2D)    |    (None, 96, 128, 512)   |   2359808   |

|block4_pool (MaxPooling2D) |  (None, 48, 64, 512)    |   0         |

|block5_conv1 (Conv2D)     |   (None, 48, 64, 512)    |   2359808   |

|block5_conv2 (Conv2D)     |   (None, 48, 64, 512)    |   2359808   |

|block5_conv3 (Conv2D)    |    (None, 48, 64, 512)    |   2359808   |

|block5_pool (MaxPooling2D) |  (None, 24, 32, 512)    |   0         |

|global_average_pooling2d_1 |( (None, 512)            |   0         |