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=============> comparison between one-stage and two-stage detector retina net v.s. faster rcnn
# FCOSHandwritten GPU022 card4
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/detection/fcos_R_50_FPN_3x_handwritten.yaml
# fasterRCNNHandwritten GPU023 card1
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/detection/faster_rcnn_R_50_FPN_3x_handwritten.yaml
# CascadeRCNN GPU021 card4
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/detection/faster_cascade_rcnn_R_50_FPN_3x_handwritten.yaml
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=============> comparison between different backbone, C4 and FPN
# (1) different backbone
# backboneC4fasterRCNNHandwritten GPU025 card3
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/detection/faster_rcnn_R_50_C4_3x_handwritten.yaml
# backboneR101C4fasterRCNNHandwritten GPU025 card4
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/detection/faster_rcnn_R_101_C4_3x_handwritten.yaml
# backboneR101FPNfasterRCNNHandwritten GPU023 card2
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/detection/faster_rcnn_R_101_FPN_3x_handwritten.yaml
# (2) pretrained or not
# notPretrainedR50FPNfasterRCNNHandwritten GPU023 card3
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/detection/faster_rcnn_R_50_notPretrained_FPN_3x_handwritten.yaml
# notPretrainedR50C4fasterRCNNHandwritten GPU025 card5
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/detection/faster_rcnn_R_50_notPretrained_C4_3x_handwritten.yaml
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=============> comparison between different anchor scale
# fasterRCNNHandwrittenDefaultAnchor GPU022 card1
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/detection/faster_rcnn_R_50_FPN_default_anchor_3x_handwritten.yaml
# fasterRCNNHandwrittenMoreAnchor GPU022 card2
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/detection/faster_rcnn_R_50_FPN_more_anchor_3x_handwritten.yaml
# fasterRCNNHandwrittenOneAnchor GPU023 card4
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/detection/faster_rcnn_R_50_FPN_one_anchor_3x_handwritten.yaml
=============> comparison between different image resolution
# fasterRCNNHandwrittenS1 GPU022 card0
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/detection/faster_rcnn_R_50_FPN_s1_3x_handwritten.yaml
# fasterRCNNHandwrittenS2 GPU022 card3
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/detection/faster_rcnn_R_50_FPN_s2_3x_handwritten.yaml
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============> Instance segmentation results on Handwritten
# maskRCNNHandwritten GPU023 card0
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/instance_segmentation/mask_rcnn_R_50_FPN_3x_handwritten.yaml
# cascadeRCNNHandwritten GPU020 card1
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/instance_segmentation/mask_cascade_rcnn_R_50_FPN_3x_handwritten.yaml
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============> Instance segmentation results on Kaiti
# maskRCNNKaiti GPU022 card0
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/instance_segmentation/mask_rcnn_R_50_FPN_3x_kaiti.yaml
# cascadeRCNNKaiti GPU021 card6
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/instance_segmentation/mask_cascade_rcnn_R_50_FPN_3x_kaiti.yaml
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==============> Cross Domain Evaluation
# maskRCNNHW2K GPU021 card1
PYTHONPATH=$PYTHONPATH:./ python -u scripts/inference_instance.py --config config/instance_segmentation/mask_rcnn_R_50_FPN_3x_handwritten_kaiti_test.yaml
# maskRCNNK2HW GPU021 card1
PYTHONPATH=$PYTHONPATH:./ python -u scripts/inference_instance.py --config config/instance_segmentation/mask_rcnn_R_50_FPN_3x_kaiti_handwritten_test.yaml
======================> Rebuttal <============================================> Rebuttal <======================
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====> cross font evaluation, kaiti, songti, heiti
PYTHONPATH=$PYTHONPATH:./ python -u scripts/inference_instance.py --config config/instance_segmentation/mask_rcnn_R_50_FPN_3x_kaiti_test.yaml
====> Instance segmentation results on noisy dataset
# maskRCNNNoisyHW GPU023 card0
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/instance_segmentation/mask_rcnn_R_50_FPN_3x_noisy_handwritten.yaml
# maskRCNNNoisyKaiti GPU023 card1
PYTHONPATH=$PYTHONPATH:./ python -u scripts/train_instance.py --config config/instance_segmentation/mask_rcnn_R_50_FPN_3x_noisy_kaiti.yaml
=====> Test model on noisy dataset
# maskRCNNKaitiNoisyTest
PYTHONPATH=$PYTHONPATH:./ python -u scripts/inference_instance.py --config config/instance_segmentation/mask_rcnn_R_50_FPN_3x_kaiti_noisy_test.yaml
# maskRCNNYHandwrittenNoisyTest
PYTHONPATH=$PYTHONPATH:./ python -u scripts/inference_instance.py --config config/instance_segmentation/mask_rcnn_R_50_FPN_3x_handwritten_noisy_test.yaml
======> Vis the failure case of model
# maskRCNNKaitiFailTest
PYTHONPATH=$PYTHONPATH:./ python -u scripts/inference_instance.py --config config/instance_segmentation/mask_rcnn_R_50_FPN_3x_kaiti_fail_test.yaml
# maskRCNNHWFailTest
PYTHONPATH=$PYTHONPATH:./ python -u scripts/inference_instance.py --config config/instance_segmentation/mask_rcnn_R_50_FPN_3x_handwritten_fail_test.yaml