DrCyZ: Techniques for analyzing and extracting useful information from CyZ.
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DrCyZ: Techniques for analyzing and extracting useful information from CyZ. Samples from NASA Perseverance and set of GAN generated synthetic images from Neural Mars. Repository: https://github.com/decurtoidiaz/drcyz <br> Subset of samples from (includes tools to visualize and analyse the dataset): CyZ: MARS Space Exploration Dataset. [https://doi.org/10.5281/zenodo.5655473] Images from NASA missions of the celestial body. Repository: https://github.com/decurtoidiaz/cyz Authors: J. de Curtò c@decurto.be I. de Zarzà z@dezarza.be ------------------------------------------<br> File Information from DrCyZ-1.1<br> ------------------------------------------ • Subset of samples from Perseverance (drcyz/c).<br> ∙ png (drcyz/c/png).<br> PNG files (5025) selected from NASA Perseverance (CyZ-1.1) after t-SNE and K-means Clustering. <br> ∙ csv (drcyz/c/csv).<br> CSV file. • Resized samples from Perseverance (drcyz/c+).<br> ∙ png 64x64; 128x128; 256x256; 512x512; 1024x1024 (drcyz/c+/drcyz_64-1024).<br> PNG files resized at the corresponding size. <br> ∙ TFRecords 64x64; 128x128; 256x256; 512x512; 1024x1024 (drcyz/c+/tfr_drcyz_64-1024).<br> TFRecord resized at the corresponding size to import on Tensorflow. • Synthetic images from Neural Mars generated using Stylegan2-ada (drcyz/drcyz+).<br> ∙ png 100; 1000; 10000 (drcyz/drcyz+/drcyz_256_100-10000)<br> PNG files subset of 100, 1000 and 10000 at size 256x256. • Network Checkpoint from Stylegan2-ada trained at size 256x256 (drcyz/model_drcyz).<br> ∙ network-snapshot-000798-drcyz.pkl • Notebooks in python to analyse the original dataset and reproduce the experiments; K-means Clustering, t-SNE, PCA, synthetic generation using Stylegan2-ada and instance segmentation using Deeplab (https://github.com/decurtoidiaz/drcyz/tree/main/dr_cyz+).<br> ∙ clustering_curiosity_de_curto_and_de_zarza.ipynb<br> K-means Clustering and PCA(2) with images from Curiosity.<br> ∙ clustering_perseverance_de_curto_and_de_zarza.ipynb<br> K-means Clustering and PCA(2) with images from Perseverance.<br> ∙ tsne_curiosity_de_curto_and_de_zarza.ipynb<br> t-SNE and PCA (components selected to explain 99% of variance) with images from Curiosity.<br> ∙ tsne_perseverance_de_curto_and_de_zarza.ipynb<br> t-SNE and PCA (components selected to explain 99% of variance) with images from Perseverance.<br> ∙ Stylegan2-ada_de_curto_and_de_zarza.ipynb<br> Stylegan2-ada trained on a subset of images from NASA Perseverance (DrCyZ).<br> ∙ statistics_perseverance_de_curto_and_de_zarza.ipynb<br> Compute statistics from synthetic samples generated by Stylegan2-ada (DrCyZ) and images from NASA Perseverance (CyZ).<br> ∙ DeepLab_TFLite_ADE20k_de_curto_and_de_zarza.ipynb<br> Example of instance segmentation using Deeplab with a sample from NASA Perseverance (DrCyZ).
