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## train ##
CUDA_VISIBLE_DEVICES=0,1 python3 tools/train.py -f exps/example/yolox_voc/yolox_dota_s_obb_kld.py -d 2 -b 16 --fp16 -c weights/yolox_s.pth.tar
## resume ##
CUDA_VISIBLE_DEVICES=0,1 python3 tools/train.py -f exps/example/yolox_voc/yolox_dota_s_obb_kld.py -d 2 -b 16 --fp16 --resume -c /home/lyy/gxw/YOLOX/YOLOX_outputs/yolox_dota_s_obb_kld/latest_ckpt.pth
## val ##
# save results as voc format only
CUDA_VISIBLE_DEVICES=0,1 python tools/eval.py -f exps/example/yolox_voc/yolox_dota_s_obb_kld.py -d 2 -b 16 -c /home/lyy/gxw/YOLOX/YOLOX_outputs/yolox_dota_s_obb_kld/latest_ckpt.pth
## test ##
# save results as voc format only (you must comment line 151 and uncomment line 152 in YOLOXOBB/yolox/data/datasets/dota_obb.py )
CUDA_VISIBLE_DEVICES=0,1 python tools/eval.py -f exps/example/yolox_voc/yolox_dota_s_obb_kld.py -d 2 -b 16 -c /home/lyy/gxw/YOLOX/YOLOX_outputs/yolox_dota_s_obb_kld/latest_ckpt.pth
## demo ##
CUDA_VISIBLE_DEVICES=0 python tools/demo_obb.py image -f exps/example/yolox_voc/yolox_dota_s_obb_kld.py -c /home/lyy/gxw/YOLOX/YOLOX_outputs/yolox_dota_s_obb_kld/latest_ckpt.pth --path assets/demo --conf 0.5 --nms 0.65 --tsize 1024 --save_result --device gpu
CUDA_VISIBLE_DEVICES=1 python tools/eval.py -f exps/example/yolox_voc/yolox_dota_s_obb_kld.py -d 1 -b 1 -c /home/lyy/gxw/YOLOX/YOLOX_outputs/yolox_dota_s_obb_kld/latest_ckpt.pth