Hi, I am trying to reproduce the result of this code base, I use cholect50 as data and follow the data split instructions. The train.sh is used as follow:
#! /bin/bash
CUDA_VISIBLE_DEVICES=0, python3 main.py
--exp_name rit_train
--log_name rit_train.log
--max_epochs 40
--early_stopping_patience 5
--fold 1
--split 'cholect45-crossval'
--m 6
--bs 16
--nw 6
--topK 5
--ln 0
--cg 0
--od1 1e-6
--od2 1e-6
--od3 1e-6
--mom 0.95
--ms1 20
--ms2 39
--ms3 60
--g1 0.94
--g2 0.95
--g3 0.99
--layers 8
--data_dir "path-to-CholecT50" \
Plus I also change the learning rate to 1e-3 to avoid the issue of under fitting. But i only got the IVT mAP as 0.1. Is there any training details i missed?
Hi, I am trying to reproduce the result of this code base, I use cholect50 as data and follow the data split instructions. The train.sh is used as follow:
#! /bin/bash
CUDA_VISIBLE_DEVICES=0, python3 main.py
--exp_name rit_train
--log_name rit_train.log
--max_epochs 40
--early_stopping_patience 5
--fold 1
--split 'cholect45-crossval'
--m 6
--bs 16
--nw 6
--topK 5
--ln 0
--cg 0
--od1 1e-6
--od2 1e-6
--od3 1e-6
--mom 0.95
--ms1 20
--ms2 39
--ms3 60
--g1 0.94
--g2 0.95
--g3 0.99
--layers 8
--data_dir "path-to-CholecT50" \
Plus I also change the learning rate to 1e-3 to avoid the issue of under fitting. But i only got the IVT mAP as 0.1. Is there any training details i missed?