From 45b1f223a30025089bab6276ae6e53043cb54b99 Mon Sep 17 00:00:00 2001 From: Justin Tan Date: Sun, 13 May 2018 01:36:57 +1000 Subject: [PATCH] loss plots --- README.md | 12 ++++++++---- config.py | 2 +- images/results/discriminator_loss.png | Bin 0 -> 376787 bytes images/results/generator_loss.png | Bin 0 -> 644698 bytes 4 files changed, 9 insertions(+), 5 deletions(-) create mode 100644 images/results/discriminator_loss.png create mode 100644 images/results/generator_loss.png diff --git a/README.md b/README.md index a74930c..d7ace62 100644 --- a/README.md +++ b/README.md @@ -27,7 +27,8 @@ $ python3 compress.py -i /path/to/image -r /path/to/model/checkpoint ``` ## Results -These globally compressed images are from the test split of the Cityscapes `leftImg8bit` dataset. +These globally compressed images are from the test split of the Cityscapes `leftImg8bit` dataset. The decoder seems to hallunicate greenery in buildings, and vice-versa. + #### C=8 channels, multiscale discriminator | Input | Output (0.072 bpp) | |-------|-------| @@ -37,10 +38,12 @@ These globally compressed images are from the test split of the Cityscapes `left ``` Show quantized C=4,8,16 channels image comparison ``` - +| Generator Loss | Discriminator Loss | +|-------|-------| +|![gen_loss](images/results/generator_loss.png) | ![discriminator_loss](images/results/discriminator_loss.png) | ## Pretrained Model -You can find pretrained models for global compression with a channel bottleneck of `C = 8` (corresponding to a 0.072 bpp representation) and multiscale discriminator loss under ``. The model was trained for 64 epochs on the train split of the [Cityscapes](https://www.cityscapes-dataset.com/) `leftImg8bit` dataset. +You can find pretrained models for global compression with a channel bottleneck of `C = 8` (corresponding to a 0.072 bpp representation) and multiscale discriminator loss under ``. The model was trained for 64 epochs on the train split of the [Cityscapes](https://www.cityscapes-dataset.com/) `leftImg8bit` dataset. * Warning: Tensorflow 1.3 was used to train the models, but it appears to load without problems on Tensorflow 1.8. ## Details / extensions @@ -78,4 +81,5 @@ Training was done using the [ADE 20k dataset](http://groups.csail.mit.edu/vision ### Resources * [Generative Adversarial Networks for Extreme Learned Image Compression](https://data.vision.ee.ethz.ch/aeirikur/extremecompression/#publication) * [CycleGAN](https://arxiv.org/pdf/1703.10593.pdf) -* [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://tcwang0509.github.io/pix2pixHD/) \ No newline at end of file +* [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://tcwang0509.github.io/pix2pixHD/) + diff --git a/config.py b/config.py index 13a955f..a330ef7 100644 --- a/config.py +++ b/config.py @@ -68,5 +68,5 @@ class directories(object): tensorboard = 'tensorboard' checkpoints = 'checkpoints' checkpoints_best = 'checkpoints/best' - samples = 'samples/cityscapes' + samples = 'samples' diff --git a/images/results/discriminator_loss.png b/images/results/discriminator_loss.png new file mode 100644 index 0000000000000000000000000000000000000000..fd97cd003a9f715f982ba921948d08c999236d7a GIT binary patch literal 376787 zcmbTd1yq#V+cr)~Nem!0bSu&f-7pLwC8#tgB|~>NNC`s<(lLM_DJ>zWAcBH0G)Rbm zG%6vj|Baq=e&>C^Z>|6Py{@Ga^UTb%_r9Zp+tLy57lut+u3;rduuL{Ka& zoHIgv@RO*hoO3KJ5*~XMm75wWDy%m>T<_aE*!9QCL%Wpd-pYqOnS~d>26E}KVCYs#4Vnrf#b!x0C>69XJNeN7yBt0pYc~t7k-NW$ zhN71jUYN+#&s+0zII-v#JB8m4Jb#5!^LHxt;$p=`OQs7h*{TH5Va=j=Qodj*u0;pz z(MwUr#wBwNrzl52Os~4N5eHs%s~}dp>ipSi3Q~rp_{6<+#0zVTy2Gfo!)r|~SgI${ zs#BfB9k-`bmhCb5ce_|M8#~=dqi%ewoUyNGCBM4uHj;G(B!USmt@Mbc)H%~^-0jW_ z){hoa^SD@YyBTS+Z$fE`V~tZ<2J9D!wIYwul42snR?e}mu}3!oFNN?vig`mm75b@v zO2{sZ2nAt!=~;w6c~;(hNxyGgz>}bP`#U_xQgG3A~0 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