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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
make ASCII Art by Deep Learning
| Date | Stars |
|---|---|
| 2026-07-31 | 1575 |
| 2026-08-08 | 1575 |
| 2026-08-09 | 1575 |
| 2026-08-13 | 1576 |
| 2026-08-16 | 1576 |
| 2026-08-17 | 1576 |
| 2026-08-18 | 1576 |
| 2026-09-03 | 1577 |
| 2026-09-04 | 1576 |
| 2026-09-07 | 1577 |
| 2026-09-13 | 1578 |
| 2026-09-26 | 1579 |
| 2026-09-30 | 1580 |
| 2026-10-02 | 1581 |
| 2026-10-09 | 1580 |
| 2026-10-12 | 1582 |
| 2026-10-14 | 1583 |
| 2026-10-18 | 1584 |
| 2026-10-20 | 1583 |
| 2026-10-25 | 1584 |
| 2026-10-31 | 1583 |
| 2026-11-01 | 1584 |
| 2026-11-02 | 1585 |
| 2026-11-04 | 1585 |
Today
— stars today
This week
+1 stars this week
This month
+4 stars this month
Momentum
0.0
growth rate 0.06%/day
DeepAA ==== This is convolutional neural networks generating ASCII art. This repository is under construction. This work is accepted by [NIPS 2017 Workshop, Machine Learning for Creativity and Design](https://nips2017creativity.github.io/) The paper: [ASCII Art Synthesis with Convolutional Networks](https://nips2017creativity.github.io/doc/ASCII_Art_Synthesis.pdf) [Web application (using previous version model)](https://tar-bin.github.io/DeepAAonWeb/) (by [tar-bin](https://github.com/tar-bin))  ## Change log + 2017/12/2 added light model ## Requirements + TensorFlow (1.3.0) + Keras (2.0.8) + NumPy (1.13.3) + Pillow (4.2.1) + Pandas (0.18.0) + Scikit-learn (0.19.0) + h5py (2.7.1) + model's weight (download it from [here](https://drive.google.com/open?id=0B90WglS_AQWebjBleG5uRXpmbUE) and place it in dir `model`.) + training data (additional, download it from [here](https://drive.google.com/open?id=1L5n5ICrsXtsWkT-aq2et1FTzp-RH3CeS), extract it and place the extracted directory in dir `data`.) ) ## How to use please change the line 15 of `output.py ` ``` image_path = 'sample images/original images/21 original.png' # put the path of the image that you convert. ``` into the path of image file that you use. You should use a grayscale line image. then run `output.py `. converted images will be output at `output/ `. You can select light model by change the line 13, 14 of `output.py ` into ``` model_path = "model/model_light.json" weight_path = "model/weight_light.hdf5" ``` ## License The pre-trained models and the other files we have provided are licensed under the MIT License.
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:fcaa34f176df938e, topic:deep-learning, topic:tensorflow