This repository is based on Swin-Transformer-Object-Detection and mmdetection. All configurations and codes were revised for MORAI dataset.
Dataset | Epoch | box AP(vehicle) | config | log | model |
---|---|---|---|---|---|
Real | 36 | 85.8 | config | log | [model] |
Daegu | 36 | 68.3 | config | log | [model] |
Sejong BRT 1 | 36 | 70.5 | config | log | [model] |
Sangam Edge | 36 | 71.1 | config | log | [model] |
Sejong BRT 1 Edge | 36 | 69.6 | config | log | [model] |
Real:
Daegu:
Sejong BRT 1:
Sangam Edge:
Sejong BRT 1 Edge:
Dataset | Epoch | Real test-set box AP(vehicle) | config | log | model |
---|---|---|---|---|---|
Daegu | 36 | 73.9 | config | log | [model] |
Sejong BRT 1 | 36 | 71.3 | config | log | [model] |
Sangam Edge | 36 | 70.3 | config | log | [model] |
Sejong BRT 1 Edge | 36 | 64.8 | config | log | [model] |
Daegu:
Sejong BRT 1:
Sangam Edge:
Sejong BRT 1 Edge:
Please refer to install.md for installation, dataset preparation and making configuration file.
# single-gpu testing
python tools/test.py {CONFIG_FILE} {MODEL_FILE} --eval bbox \
(--show-dir {LOCATION}) \
(--options "classwise=True")
# multi-gpu testing
(CUDA_VISIBLE_DEVICES={GPU_NUM}) \
tools/dist_test.sh {CONFIG_FILE} {MODEL_FILE} {TOTAL_NUM_OF_GPU} --eval bbox \
(--show-dir {LOCATION}) \
(--options “classwise=True”)
--show-dir saves pictures of result, --options "classwise=True" shows average precision of all classes. You can use --show in GUI environment.
Example:
python tools/test.py \
configs/swin/cascade_mask_rcnn_swin_base_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_morai_daegu.py \
checkpoints/cascade_mask_rcnn_swin_base_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_morai_daegu.pth \
--eval bbox --show-dir result.bbox.daegu/ --options “classwise=True”
CUDA_VISIBLE_DEVICES=0,1,3 tools/dist_test.sh \
configs/swin/cascade_mask_rcnn_swin_base_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_morai_daegu.py \
checkpoints/cascade_mask_rcnn_swin_base_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_morai_daegu.pth 3 \
--eval bbox --show-dir result.bbox.daegu/ --options “classwise=True”
# single-gpu training
python tools/train.py {CONFIG_FILE}
# multi-gpu training
(CUDA_VISIBLE_DEVICES={GPU_NUM}) tools/dist_train.sh {CONFIG_FILE} {TOTAL_NUM_OF_GPU}
Example:
python tools/train.py configs/swin/cascade_mask_rcnn_swin_base_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_morai_daegu.py
CUDA_VISIBLE_DEVICES=0,1,3 tools/dist_train.sh \
configs/swin/cascade_mask_rcnn_swin_base_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_morai_daegu.py 3