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Name: change to whatever you want, removing the “debug” from the name Go to codes/options/train/train_ESRGAN.json and make the following changes:
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If you have GIMP installed, you can also download a batch manipulation plugin called BIMP and process the image that way.ħ. Make sure that both the LR and HR images have the same format and filename. It also helps make the results smoother overall. You may want to check “Change Color Depth” or add some JPG compression, noise or dithering if you’re specifically training it for low quality images. Download and open InfranView ( ), press B to open the batch convert dialog, check “Use advanced options” and then click “Advanced” button to access the resize settings. You will need to batch convert these HR tiles to 4x downscaled versions. Repeat this process for the validation images.Ħ. Save_folder = 'C:\Users\Username\BasicSR-master\General100_tiles’ĭouble click to run it. Input_folder = ‘C:\Users\Username\BasicSR-master\General100’ If you’re using Windows, replace all the slashes (“") with double slashes, as ”" is an escape character.
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Open up codes/scripts/extract_subimgs_single.py.Ĭhange crop_sz to 192 or 128 (I’d stick to the latter unless you have a beefy graphics card), input_folder to the full path name of your image folder, and save_folder to where you want to save the tiles to. You will also need to convert your dataset into fixed tiles. The world in which WipEout 2048 takes place in is grittier than what’s come before it simply because we’re entering into it when the sport is still at a nascent stage. Take about 5-10% of your images and put them in a separate folder these will be your validation images.ĥ. You can use InfranView or BIMP (see below) to convert the images to RGB.Ĥ You will need to split your “training” and “validation” images. RGB only, or else you will get a “Sizes of tensors must match” error. Make absolutely sure that none of the images are greyscale or indexed, or have alpha channels. 1000 tiles (see step 5) are the absolute minimum for getting good results, but the more, the better. The BasicSR creator uploaded several datasets to use here, but there’s plenty of other datasets you can use online. Place the models in (BasicSR directory)/experiments/pretrained_modelsģ. Download BasicSR and the ESRGAN pretrained models.
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Then go to the command line and paste in this: pip install numpy opencv-python lmdbĢ. It’s needed for one of BasicSR’s dependencies. If you’ve already done all that, go to Step 1.ġ: Download and install Microsoft Build Tools 2015. Everything needed to test ESRGAN is also needed to train it - Python, CUDA, etc. If you haven’t gotten ESRGAN set up for testing, please read this. Note: ESRGAN training appears to be slower on Windows than Linux by around 5x, at least on my machine.