Models: - Name: fcn_unet_s5-d16_64x64_40k_drive In Collection: FCN Metadata: inference time (ms/im): - value: None hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: DRIVE Metrics: mIoU: 0.680 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-26cee593.pth Config: configs/unet-s5-d16/fcn_unet_s5-d16_64x64_40k_drive.py - Name: pspnet_unet_s5-d16_64x64_40k_drive In Collection: PSPNet Metadata: inference time (ms/im): - value: None hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: DRIVE Metrics: mIoU: 0.599 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth Config: configs/unet-s5-d16/pspnet_unet_s5-d16_64x64_40k_drive.py - Name: deeplabv3_unet_s5-d16_64x64_40k_drive In Collection: DeepLabV3 Metadata: inference time (ms/im): - value: None hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: DRIVE Metrics: mIoU: 0.596 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_64x64_40k_drive.py - Name: fcn_unet_s5-d16_128x128_40k_stare In Collection: FCN Metadata: inference time (ms/im): - value: None hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: STARE Metrics: mIoU: 0.968 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-6ea7cfda.pth Config: configs/unet-s5-d16/fcn_unet_s5-d16_128x128_40k_stare.py - Name: pspnet_unet_s5-d16_128x128_40k_stare In Collection: PSPNet Metadata: inference time (ms/im): - value: None hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: STARE Metrics: mIoU: 0.982 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth Config: configs/unet-s5-d16/pspnet_unet_s5-d16_128x128_40k_stare.py - Name: deeplabv3_unet_s5-d16_128x128_40k_stare In Collection: DeepLabV3 Metadata: inference time (ms/im): - value: None hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: STARE Metrics: mIoU: 0.999 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_128x128_40k_stare.py - Name: fcn_unet_s5-d16_128x128_40k_chase_db1 In Collection: FCN Metadata: inference time (ms/im): - value: None hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: CHASE_DB1 Metrics: mIoU: 0.968 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-95852f45.pth Config: configs/unet-s5-d16/fcn_unet_s5-d16_128x128_40k_chase_db1.py - Name: pspnet_unet_s5-d16_128x128_40k_chase_db1 In Collection: PSPNet Metadata: inference time (ms/im): - value: None hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: CHASE_DB1 Metrics: mIoU: 0.982 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth Config: configs/unet-s5-d16/pspnet_unet_s5-d16_128x128_40k_chase_db1.py - Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1 In Collection: DeepLabV3 Metadata: inference time (ms/im): - value: None hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: CHASE_DB1 Metrics: mIoU: 0.999 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py - Name: fcn_unet_s5-d16_256x256_40k_hrf In Collection: FCN Metadata: inference time (ms/im): - value: None hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: HRF Metrics: mIoU: 2.525 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-df3ec8c4.pth Config: configs/unet-s5-d16/fcn_unet_s5-d16_256x256_40k_hrf.py - Name: pspnet_unet_s5-d16_256x256_40k_hrf In Collection: PSPNet Metadata: inference time (ms/im): - value: None hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: HRF Metrics: mIoU: 2.588 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth Config: configs/unet-s5-d16/pspnet_unet_s5-d16_256x256_40k_hrf.py - Name: deeplabv3_unet_s5-d16_256x256_40k_hrf In Collection: DeepLabV3 Metadata: inference time (ms/im): - value: None hardware: V100 backend: PyTorch batch size: 1 mode: FP32 Results: - Task: Semantic Segmentation Dataset: HRF Metrics: mIoU: 2.604 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth Config: configs/unet-s5-d16/deeplabv3_unet_s5-d16_256x256_40k_hrf.py