benchmark_train_models.txt

bisenetv1/bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512.py
bisenetv2/bisenetv2_fcn_4xb4-ohem-160k_cityscapes-1024x1024.py
deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py
deeplabv3/deeplabv3_r101-d8_4xb4-160k_ade20k-512x512.py
deeplabv3plus/deeplabv3plus_r101-d8_4xb2-40k_cityscapes-769x769.py
deeplabv3plus/deeplabv3plus_r50-d8_4xb4-80k_ade20k-512x512.py
hrnet/fcn_hr18s_4xb4-80k_ade20k-512x512.py
hrnet/fcn_hr18_4xb2-160k_cityscapes-512x1024.py
knet/knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-512x512.py
knet/knet-s3_r50-d8_pspnet_8xb2-adamw-80k_ade20k-512x512.py
mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py
mask2former/mask2former_swin-l-in22k-384x384-pre_8xb2-160k_ade20k-640x640.py
maskformer/maskformer_swin-s_upernet_8xb2-160k_ade20k-512x512.py
mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py
ocrnet/ocrnet_hr48_4xb4-80k_ade20k-512x512.py
pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes.py
pspnet/pspnet_r50-d8-rsb_4xb2-adamw-80k_cityscapes-512x1024.py
pspnet/pspnet_r101-d8_4xb4-80k_ade20k-512x512.py
segformer/segformer_mit-b5_8xb2-160k_ade20k-512x512.py
segmenter/segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py
segnext/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py
swin/swin-base-patch4-window12-in22k-384x384-pre_upernet_8xb2-160k_ade20k-512x512.py
twins/twins_pcpvt-l_uperhead_8xb2-160k_ade20k-512x512.py
unet/unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024.py
upernet/upernet_r101_4xb2-40k_cityscapes-512x1024.py