fix typo in experiments
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d133939e9e
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@ -1,8 +1,8 @@
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cd src
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cd src
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# train
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# train
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python main.py multi_pose --exp_id dla_1x --batch_size 128 --master_batch 9 --lr 5e-4 --load_model ../models/ctdet_coco_dla_2x.pth --gpus 0,1,2,3,4,5,6,7 --num_workers 16
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python main.py multi_pose --exp_id dla_1x --dataset coco_hp --batch_size 128 --master_batch 9 --lr 5e-4 --load_model ../models/ctdet_coco_dla_2x.pth --gpus 0,1,2,3,4,5,6,7 --num_workers 16
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# test
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# test
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python test.py multi_pose --exp_id dla_1x --keep_res --resume
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python test.py multi_pose --exp_id dla_1x --dataset coco_hp --keep_res --resume
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# flip test
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# flip test
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python test.py multi_pose --exp_id dla_1x --keep_res --resume --flip_test
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python test.py multi_pose --exp_id dla_1x --dataset coco_hp --keep_res --resume --flip_test
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cd ..
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cd ..
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@ -1,10 +1,10 @@
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cd src
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cd src
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# train
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# train
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python main.py multi_pose --exp_id dla_3x --batch_size 128 --master_batch 9 --lr 5e-4 --load_model ../models/ctdet_coco_dla_2x.pth --gpus 0,1,2,3,4,5,6,7 --num_workers 16 --num_epochs 320 lr_step 270,300
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python main.py multi_pose --exp_id dla_3x --dataset coco_hp --batch_size 128 --master_batch 9 --lr 5e-4 --load_model ../models/ctdet_coco_dla_2x.pth --gpus 0,1,2,3,4,5,6,7 --num_workers 16 --num_epochs 320 lr_step 270,300
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# or use the following command if your have dla_1x trained
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# or use the following command if your have dla_1x trained
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# python main.py multi_pose --exp_id dla_3x --batch_size 128 --master_batch 9 --lr 5e-4 --gpus 0,1,2,3,4,5,6,7 --num_workers 16 --load_model ../exp/multi_pose/dla_1x/model_90.pth --resume
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# python main.py multi_pose --exp_id dla_3x --dataset coco_hp --batch_size 128 --master_batch 9 --lr 5e-4 --gpus 0,1,2,3,4,5,6,7 --num_workers 16 --load_model ../exp/multi_pose/dla_1x/model_90.pth --resume
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# test
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# test
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python test.py multi_pose --exp_id dla_3x --keep_res --resume
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python test.py multi_pose --exp_id dla_3x --dataset coco_hp --keep_res --resume
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# flip test
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# flip test
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python test.py multi_pose --exp_id dla_3x --keep_res --resume --flip_test
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python test.py multi_pose --exp_id dla_3x --dataset coco_hp --keep_res --resume --flip_test
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cd ..
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cd ..
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@ -1,8 +1,8 @@
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cd src
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cd src
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# train
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# train
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python main.py multi_pose --exp_id hg_1x --arch hourglass --batch_size 24 --master_batch 4 --lr 2.5e-4 --load_model ../models/ctdet_coco_hg.pth --gpus 0,1,2,3,4 --num_epochs 50 --lr_step 40
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python main.py multi_pose --exp_id hg_1x --dataset coco_hp --arch hourglass --batch_size 24 --master_batch 4 --lr 2.5e-4 --load_model ../models/ctdet_coco_hg.pth --gpus 0,1,2,3,4 --num_epochs 50 --lr_step 40
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# test
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# test
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python test.py multi_pose --exp_id hg_1x --arch hourglass --keep_res --resume
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python test.py multi_pose --exp_id hg_1x --dataset coco_hp --arch hourglass --keep_res --resume
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# flip test
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# flip test
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python test.py multi_pose --exp_id hg_1x --arch hourglass --keep_res --resume --flip_test
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python test.py multi_pose --exp_id hg_1x --dataset coco_hp --arch hourglass --keep_res --resume --flip_test
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cd ..
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cd ..
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@ -1,10 +1,10 @@
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cd src
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cd src
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# train
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# train
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python main.py multi_pose --exp_id hg_3x --arch hourglass --batch_size 24 --master_batch 4 --lr 2.5e-4 -load_model ../models/ctdet_coco_hg.pth --gpus 0,1,2,3,4 --num_epochs 150 --lr_step 130
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python main.py multi_pose --exp_id hg_3x --dataset coco_hp --arch hourglass --batch_size 24 --master_batch 4 --lr 2.5e-4 -load_model ../models/ctdet_coco_hg.pth --gpus 0,1,2,3,4 --num_epochs 150 --lr_step 130
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# or use the following command if your have dla_1x trained
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# or use the following command if your have dla_1x trained
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# python main.py multi_pose --exp_id hg_3x --arch hourglass --batch_size 24 --master_batch 4 --lr 2.5e-4 --gpus 0,1,2,3,4 --num_epochs 150 --lr_step 130 --load_model ../exp/multi_pose/hg_1x/model_40.pth --resume
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# python main.py multi_pose --exp_id hg_3x --dataset coco_hp --arch hourglass --batch_size 24 --master_batch 4 --lr 2.5e-4 --gpus 0,1,2,3,4 --num_epochs 150 --lr_step 130 --load_model ../exp/multi_pose/hg_1x/model_40.pth --resume
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# test
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# test
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python test.py multi_pose --exp_id hg_3x --arch hourglass --keep_res --resume
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python test.py multi_pose --exp_id hg_3x --dataset coco_hp --arch hourglass --keep_res --resume
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# flip test
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# flip test
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python test.py multi_pose --exp_id hg_3x --arch hourglass --keep_res --resume --flip_test
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python test.py multi_pose --exp_id hg_3x --dataset coco_hp --arch hourglass --keep_res --resume --flip_test
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cd ..
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cd ..
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