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AFMF-TIFS15
AFMF-TIFS15
 
 
__pycache__
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data_folders_png_files
data_folders_png_files
 
 
grayscale_dataset
grayscale_dataset
 
 
mf_dataset
mf_dataset
 
 
mfig
mfig
 
 
pokemon_dataset/images
pokemon_dataset/images
 
 
predicted
predicted
 
 
README.md
README.md
 
 
best_model.pth
best_model.pth
 
 
dataloader.py
dataloader.py
 
 
file_autoencoder_bce_20_epochs.log
file_autoencoder_bce_20_epochs.log
 
 
file_autoencoder_bce_20_epochs_bs_9.log
file_autoencoder_bce_20_epochs_bs_9.log
 
 
file_autoencoder_mse_20_epochs_bs_9.log
file_autoencoder_mse_20_epochs_bs_9.log
 
 
median filtering detection using unet and deconvolution.pdf
median filtering detection using unet and deconvolution.pdf
 
 
mse unet output plot.py
mse unet output plot.py
 
 
predict_img_colab.py
predict_img_colab.py
 
 
pytorch_env.yml
pytorch_env.yml
 
 
requirements.txt
requirements.txt
 
 
run_unet.py
run_unet.py
 
 
size_check.py
size_check.py
 
 
split_random_dataset.py
split_random_dataset.py
 
 
u-net_predicted_togepi.png
u-net_predicted_togepi.png
 
 
unet.py
unet.py
 
 

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Median Filtering Detection using U-Net and Deconvolution

  • Presented By Akshat Arora (B.Tech 2021-25) @IIT_Bhilai

the dataset generated at different steps are mentioned in the presentation pdf. the path to the dataset are given at the start of the code files. Modify the path to dataset directories to that of the actual paths in your system.

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