DrCyZ: Techniques for analyzing and extracting useful information from CyZ.
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下载链接：
https://zenodo.org/record/5816857
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资源简介：
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
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
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File Information from DrCyZ-1.1
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• Subset of samples from Perseverance (drcyz/c).
∙ png (drcyz/c/png).
PNG files (5025) selected from NASA Perseverance (CyZ-1.1) after t-SNE and K-means Clustering.
∙ csv (drcyz/c/csv).
CSV file.
• Resized samples from Perseverance (drcyz/c+).
∙ png 64x64; 128x128; 256x256; 512x512; 1024x1024 (drcyz/c+/drcyz_64-1024).
PNG files resized at the corresponding size.
∙ TFRecords 64x64; 128x128; 256x256; 512x512; 1024x1024 (drcyz/c+/tfr_drcyz_64-1024).
TFRecord resized at the corresponding size to import on Tensorflow.
• Synthetic images from Neural Mars generated using Stylegan2-ada (drcyz/drcyz+).
∙ png 100; 1000; 10000 (drcyz/drcyz+/drcyz_256_100-10000)
PNG files subset of 100, 1000 and 10000 at size 256x256.
• Network Checkpoint from Stylegan2-ada trained at size 256x256 (drcyz/model_drcyz).
∙ 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+).
∙ clustering_curiosity_de_curto_and_de_zarza.ipynb
K-means Clustering and PCA(2) with images from Curiosity.
∙ clustering_perseverance_de_curto_and_de_zarza.ipynb
K-means Clustering and PCA(2) with images from Perseverance.
∙ tsne_curiosity_de_curto_and_de_zarza.ipynb
t-SNE and PCA (components selected to explain 99% of variance) with images from Curiosity.
∙ tsne_perseverance_de_curto_and_de_zarza.ipynb
t-SNE and PCA (components selected to explain 99% of variance) with images from Perseverance.
∙ Stylegan2-ada_de_curto_and_de_zarza.ipynb
Stylegan2-ada trained on a subset of images from NASA Perseverance (DrCyZ).
∙ statistics_perseverance_de_curto_and_de_zarza.ipynb
Compute statistics from synthetic samples generated by Stylegan2-ada (DrCyZ) and images from NASA Perseverance (CyZ).
∙ DeepLab_TFLite_ADE20k_de_curto_and_de_zarza.ipynb
Example of instance segmentation using Deeplab with a sample from NASA Perseverance (DrCyZ).
创建时间：
2022-01-19
