https://github.com/avik-pal/deepdream.jl
Implementation of Google's Deep Dream in Julia using Flux
Science Score: 10.0%
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Low similarity (11.3%) to scientific vocabulary
Keywords
deep-learning
deepdream
flux
generated-dreams
julia
machine-learning
visualization
Last synced: 9 months ago
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Implementation of Google's Deep Dream in Julia using Flux
Basic Info
Statistics
- Stars: 3
- Watchers: 4
- Forks: 2
- Open Issues: 1
- Releases: 0
Topics
deep-learning
deepdream
flux
generated-dreams
julia
machine-learning
visualization
Created about 8 years ago
· Last pushed over 7 years ago
https://github.com/avik-pal/DeepDream.jl/blob/master/
# DeepDream
**NOTE : This project was meant for Julia 0.6. So it will fail to work
with Julia 1.0 and the latest tagged version of Flux. However, an update
is WIP**
**NOTE : If there is no GPU support available please checkout the cpu
branch of this repo. The cpu code is not thoroughly tested.**
## INSTALLATION INSTRUCTIONS
Run this command in the `Julia REPL`
```julia
julia> Pkg.clone("https://github.com/avik-pal/DeepDream.jl.git")
```
## USAGE INSTRUCTIONS
1. Inorder to generate dreams without using octaves run the following
command with your own parameters
```julia
julia> img = load_image("./examples/sky.jpg")
julia> load_model(5)
julia> DeepDream.make_step(img, 10, 0.005, true, "./examples/sky_dream_new.jpg")
```
Make sure to pass all the arguments to the make_step function call to
avoid errors. Refer to the [function definition](https://github.com/avik-pal/DeepDream.jl/blob/11ef038ec6333114e521c6d6b422a4831c6bb0c8/src/dream.jl#L5) to understand what each parameter means.
2. To make use of octaves run the following commands
```julia
julia> img = load_image("./examples/sky.jpg")
julia> load_model(5)
julia> deepdream(img, 10, 0.005, 1.4, 4, "./examples/sky_dream_new.jpg")
```
Also be sure to checkout the [function definition](https://github.com/avik-pal/DeepDream.jl/blob/11ef038ec6333114e521c6d6b422a4831c6bb0c8/src/dream.jl#L27)
3. Incase you want to use any other model than the VGG19 model make sure
to pass a function to `load_model()` which returns the model you want
to use
4. To generate guided dreams run the following code.
```julia
julia> guide = load_guide_image("./examples/rio.jpg")
julia> img = load_image("./examples/sky.jpg")
julia> load_model(5)
julia> deepdream(img, 10, 0.005, 1.4, 4, "./examples/sky_dream_guided_new.jpg", guided = true)
```
The `guided_step` function might be used independently like the
`make_step` function.
5. Even easier method is to place all your images in a `./images`
directory and call the function `dream_batch` function. Send a
guiding image if necessary.
6. `recurdream` might be used to generate frames from a given image and
using `writevideo` after that generates a video file.
More indepth documentation is available for each and every function.
## SOME EXAMPLES
|Original Image|Generated Image|
|:---:|:---:|
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|Original Image|Guiding Image|Generated Image|
|:---:|:---:|:---:|
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## IMPLEMENTED
1. Utilities to load, save and generate images
2. Perform operations on Image
* Zoom
3. Utilities to load models
4. Deep Dream Generator (non-guided)
5. Generate deep dreams using Octaves
6. Guided Deep Dreams
Owner
- Name: Avik Pal
- Login: avik-pal
- Kind: user
- Location: Cambridge, MA
- Company: Massachusetts Institute of Technology
- Website: https://avik-pal.github.io
- Twitter: avikpal1410
- Repositories: 46
- Profile: https://github.com/avik-pal
PhD Student @mit || Prev: BTech CSE IITK
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| Name | Commits | |
|---|---|---|
| Avik Pal | a****l@i****n | 41 |
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