Closed ProvenceStar closed 8 months ago
Hi, I appreciate your great work, however, when I run evaluation on PACO dataset follow your instructions, I found out that both r_50 and swinbase model reach a higher AP than your paper on open-vocabulary task, the details are as follows:
and my evaluation command is: python train_net.py --config-file configs/paco/r50_paco.yaml --eval-only --num-gpus 8 and: python train_net.py --config-file configs/paco/swinbase_cascade_paco.yaml --eval-only --num-gpus 8
Could you please check your result or inform me that how should I reproduce your result, thanks!
Hi, I appreciate your great work, however, when I run evaluation on PACO dataset follow your instructions, I found out that both r_50 and swinbase model reach a higher AP than your paper on open-vocabulary task, the details are as follows:
and my evaluation command is: python train_net.py --config-file configs/paco/r50_paco.yaml --eval-only --num-gpus 8 and: python train_net.py --config-file configs/paco/swinbase_cascade_paco.yaml --eval-only --num-gpus 8
Could you please check your result or inform me that how should I reproduce your result, thanks!