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ReduNet
Hi,
Thank you for publishing your code with the paper. It's very nice work! In section 5.2 the authors discuss backpropagation training with redunet. Is code for training with backprop published in the repo?
Thanks,
Matt
run conda create --name redunet_official --file requirements.txt
on win10 or github's codespace
Collecting package metadata (current_repodata.json): done
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: failed
PackagesNotFoundError: The following packages are not available from current channels:
- lz4-c==1.9.2=h79c402e_3
- pysocks==1.7.1=py37hecd8cb5_0
- openssl==1.1.1k=h9ed2024_0
- tornado==6.0.4=py37h1de35cc_1
- libopus==1.3.1=h1de35cc_0
- nettle==3.4.1=h3018a27_0
- brotlipy==0.7.0=py37h9ed2024_1003
- torchaudio==0.8.0=py37
- ninja==1.10.1=py37h879752b_0
- libgfortran==3.0.1=h93005f0_2
- mkl-service==2.3.0=py37hfbe908c_0
- libtiff==4.1.0=hcb84e12_1
- mkl_random==1.1.1=py37h959d312_0
- pytorch==1.8.0=py3.7_0
- libuv==1.40.0=haf1e3a3_0
- opencv-python==4.4.0.44=pypi_0
- lame==3.100=h1de35cc_0
- scikit-learn==0.23.2=py37h959d312_0
- llvm-openmp==10.0.0=h28b9765_0
- gettext==0.19.8.1=hb0f4f8b_2
- chardet==4.0.0=py37hecd8cb5_1003
- intel-openmp==2019.4=233
- lcms2==2.11=h92f6f08_0
- bzip2==1.0.8=h1de35cc_0
- libffi==3.3=hb1e8313_2
- torchvision==0.9.0=py37_cpu
- mkl==2019.4=233
- ca-certificates==2021.1.19=hecd8cb5_1
- x264==1!157.20191217=h1de35cc_0
- libedit==3.1.20191231=h1de35cc_1
- freetype==2.10.4=ha233b18_0
- libiconv==1.16=h1de35cc_0
- pillow==8.0.0=py37h1a82f1a_0
- xz==5.2.5=h1de35cc_0
- python==3.7.9=h26836e1_0
- scipy==1.5.2=py37h912ce22_0
- tk==8.6.10=hb0a8c7a_0
- gnutls==3.6.5=h91ad68e_1002
- pandas==1.1.3=py37hb1e8313_0
- setuptools==50.3.0=py37h0dc7051_1
- gmp==6.1.2=hb37e062_1
- appnope==0.1.0=py37_0
- ncurses==6.2=h0a44026_1
- zeromq==4.3.3=hb1e8313_3
- sqlite==3.33.0=hffcf06c_0
- cffi==1.14.4=py37h2125817_0
- zstd==1.4.5=h41d2c2f_0
- numpy==1.19.1=py37h3b9f5b6_0
- libvpx==1.7.0=h378b8a2_0
- numpy-base==1.19.1=py37hcfb5961_0
- zlib==1.2.11=h1de35cc_3
- readline==8.0=h1de35cc_0
- ffmpeg==4.2.2=h97e5cf8_0
- pyzmq==19.0.2=py37hb1e8313_1
- openh264==2.1.0=hd9629dc_0
- kiwisolver==1.2.0=py37h04f5b5a_0
- libpng==1.6.37=ha441bb4_0
- jpeg==9b=he5867d9_2
- matplotlib-base==3.3.2=py37h181983e_0
- certifi==2020.12.5=py37hecd8cb5_0
- mkl_fft==1.2.0=py37hc64f4ea_0
- libsodium==1.0.18=h1de35cc_0
- cryptography==3.3.1=py37hbcfaee0_0
Current channels:
- https://repo.anaconda.com/pkgs/main/linux-64
- https://repo.anaconda.com/pkgs/main/noarch
- https://repo.anaconda.com/pkgs/r/linux-64
- https://repo.anaconda.com/pkgs/r/noarch
- https://conda.anaconda.org/conda-forge/linux-64
- https://conda.anaconda.org/conda-forge/noarch
To search for alternate channels that may provide the conda package you're
looking for, navigate to
https://anaconda.org
and use the search bar at the top of the page.```
Hi,
Very impressed by your work. And I have been wondering since the ReduNet is a white-box, one should be able to write down what is the uncertainty of the ReduNet's prediction analytically. Say in the test phase, I feed an image of half apple half orange to the ReduNet (which is trained to classify apple and orange), I should be able to get the prediction uncertainty for free? And in theory, I should also be able to track back through every layer to see how the uncertainty propagate, right? Is uncertainty estimation in your roadmap?
If the data format is slightly larger, the memory will be very large. Do you have any suggestions for optimization? Any suggestions are welcome, thank you!
Current requirements.txt is for os-x only.
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