HRMA-Net: High-Resolution Multi-Scale Attention Network with Full-attention Structure for Abdominal Tumor Segmentation
input dataset: 256x256 2d image
main : train_ours.py
test : val_ours.py
figure will avaliable later
module will be published after our paper is accepted！
name: HRMA-Net channels:
- defaults dependencies:
- _libgcc_mutex=0.1=main
- _openmp_mutex=4.5=1_gnu
- ca-certificates=2021.10.26=h06a4308_2
- certifi=2021.5.30=py36h06a4308_0
- ld_impl_linux-64=2.35.1=h7274673_9
- libffi=3.3=he6710b0_2
- libgcc-ng=9.3.0=h5101ec6_17
- libgomp=9.3.0=h5101ec6_17
- libstdcxx-ng=9.3.0=hd4cf53a_17
- ncurses=6.3=h7f8727e_2
- openssl=1.1.1l=h7f8727e_0
- pip=21.2.2=py36h06a4308_0
- python=3.6.13=h12debd9_1
- readline=8.1=h27cfd23_0
- setuptools=58.0.4=py36h06a4308_0
- sqlite=3.36.0=hc218d9a_0
- tk=8.6.11=h1ccaba5_0
- wheel=0.37.0=pyhd3eb1b0_1
- xz=5.2.5=h7b6447c_0
- zlib=1.2.11=h7b6447c_3
- pip:
- addict==2.4.0
- dataclasses==0.8
- mmcv-full==1.2.7
- numpy==1.19.5
- opencv-python==4.5.1.48
- perceptual==0.1
- pillow==8.4.0
- scikit-image==0.17.2
- scipy==1.5.4
- tifffile==2020.9.3
- timm==0.3.2
- torch==1.7.1
- torchvision==0.8.2
- typing-extensions==4.0.0
- yapf==0.31.0 prefix: /home/jeyamariajose/anaconda3/envs/transweather