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@@ -110,16 +110,40 @@ Oh and I swear I'll document the code properly...eventually. Admittedly I'm *on
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### Getting Started Yourself
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The easiest way to get started is to simply try out colorization here on Colab: https://colab.research.google.com/github/jantic/DeOldify/blob/master/DeOldify_colab.ipynb. This was contributed by Matt Robinson, and it's simply awesome.
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-#### On Your Own Machine
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-This project is built around the wonderful Fast.AI library. Unfortunately, it's the -old- version and I have yet to upgrade it to the new version. (That's definitely on the agenda.) So prereqs, in summary:
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-* ***Old* Fast.AI library (version 0.7)** [**UPDATE 11/7/2018**] Easiest thing to do in my mind is just to take the fastai/fastai folder and drop it in the root of this project, right next to fasterai's folder. Just today, I found this thread on installing fast.ai 0.7- This is probably your best resource on this subject! https://forums.fast.ai/t/fastai-v0-7-install-issues-thread/24652 . Do this first- this will take you most of the way, including dependencies.
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+
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+#### Your Own Machine- Hardware and Operating System Requirements
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+
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+* **(Training Only) BEEFY Graphics card**. I'd really like to have more memory than the 11 GB in my GeForce 1080TI (11GB). You'll have a tough time with less. The Unet and Critic are ridiculously large but honestly I just kept getting better results the bigger I made them.
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+* **(Colorization Alone) A decent graphics card**. You'll benefit from having more memory in a graphics card in terms of the quality of the output achievable by. Now what the term "decent" means exactly...I'm going to say 6GB +. I haven't tried it but in my head the math works....
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+* **Linux (or maybe Windows 10)** I'm using Ubuntu 16.04, but nothing about this precludes Windows 10 support as far as I know. I just haven't tested it and am not going to make it a priority for now.
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+#### Your Own Machine- Easy Install
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+You should now be able to do a simple install with Anaconda. Here are the steps:
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+
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+Open the command line and navigate to the root folder you wish to install. Then type the following commands
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+
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+```console
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+git clone https://github.com/jantic/DeOldify.git DeOldify
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+cd DeOldify
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+conda env create -f environment.yml
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+source activate deoldify
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+jupyter lab
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+```
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+Then from there you can start running the notebooks in Jupyter Lab, via the url they provide you in the console.
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+**Disclaimer**: This conda install process is new- I did test it locally but the classic developer's excuse is "well it works on my machine!" I'm keeping that in mind- there's a good chance it doesn't necessarily work on others's machines! I probably, most definitely did something wrong here. Definitely, in fact. Please let me know via opening an issue. Pobody's nerfect.
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+
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+#### Your Own Machine- More Details for Those So Inclined
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+
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+This project is built around the wonderful Fast.AI library. Unfortunately, it's the -old- version and I have yet to upgrade it to the new version. (That's definitely [update 11/18/2018: maybe] on the agenda.) So prereqs, in summary:
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+* ***Old* Fast.AI library (version 0.7)** [**UPDATE 11/18/2018**] A forked version is now bundled with the project, for ease of deployment and independence from whatever happens to the old version from here on out.
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+* **Python 3.6**
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* **Pytorch 0.4.1** (needs spectral_norm, so latest stable release is needed). https://pytorch.org/get-started/locally/
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* **Jupyter Lab** `conda install -c conda-forge jupyterlab`
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* **Tensorboard** (i.e. install Tensorflow) and **TensorboardX** (https://github.com/lanpa/tensorboardX). I guess you don't *have* to but man, life is so much better with it. And I've conveniently provided hooks/callbacks to automatically write all kinds of stuff to tensorboard for you already! The notebooks have examples of these being instantiated (or commented out since I didn't really need the ones doing histograms of the model weights). Notably, progress images will be written to Tensorboard every 200 iterations by default, so you get a constant and convenient look at what the model is doing. `conda install -c anaconda tensorflow-gpu`
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* **ImageNet** – Only if training of course. It proved to be a great dataset. http://www.image-net.org/download-images
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-* **(Training Only) BEEFY Graphics card**. I'd really like to have more memory than the 11 GB in my GeForce 1080TI (11GB). You'll have a tough time with less. The Unet and Critic are ridiculously large but honestly I just kept getting better results the bigger I made them.
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-* **(Colorization Alone) A decent graphics card**. You'll benefit from having more memory in a graphics card in terms of the quality of the output achievable by .
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-* **Linux** (I'm using Ubuntu 16.04) is assumed, but nothing from the above precludes Windows 10 support as far as I know. I just haven't tested it and am not going to make it a priority for now.
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### Pretrained Weights
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To start right away with your own images without training the model yourself, [download the weights here](https://www.dropbox.com/s/7r2wu0af6okv280/colorize_gen_192.h5) (right click and download from this link). Then open the [ColorizationVisualization.ipynb](ColorizationVisualization.ipynb) in Jupyter Lab. Make sure that there's this sort of line in the notebook referencing the weights:
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Jason Antic