Models: - Name: mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024 In Collection: FCN Results: Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 71.19 mIoU(ms+flip): 73.34 Config: configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024.py Metadata: Training Data: Cityscapes Batch Size: 8 Architecture: - M-V2-D8 - FCN Training Resources: 4x A100 GPUS Memory (GB): 3.4 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024-20230224_185436-13fef4ea.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024/mobilenet-v2-d8_fcn_4xb2-80k_cityscapes-512x1024_20230224_185436.json Paper: Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks' URL: https://arxiv.org/abs/1801.04381 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14 Framework: PyTorch - Name: mobilenet-v2-d8_pspnet_4xb2-80k_cityscapes-512x1024 In Collection: PSPNet Results: Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 70.23 Config: configs/mobilenet_v2/mobilenet-v2-d8_pspnet_4xb2-80k_cityscapes-512x1024.py Metadata: Training Data: Cityscapes Batch Size: 8 Architecture: - M-V2-D8 - PSPNet Training Resources: 4x V100 GPUS Memory (GB): 3.6 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-19e81d51.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes-20200825_124817.log.json Paper: Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks' URL: https://arxiv.org/abs/1801.04381 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14 Framework: PyTorch - Name: mobilenet-v2-d8_deeplabv3_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3 Results: Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 73.84 Config: configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb2-80k_cityscapes-512x1024.py Metadata: Training Data: Cityscapes Batch Size: 8 Architecture: - M-V2-D8 - DeepLabV3 Training Resources: 4x V100 GPUS Memory (GB): 3.9 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes-20200825_124836.log.json Paper: Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks' URL: https://arxiv.org/abs/1801.04381 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14 Framework: PyTorch - Name: mobilenet-v2-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024 In Collection: DeepLabV3+ Results: Task: Semantic Segmentation Dataset: Cityscapes Metrics: mIoU: 75.2 Config: configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3plus_4xb2-80k_cityscapes-512x1024.py Metadata: Training Data: Cityscapes Batch Size: 8 Architecture: - M-V2-D8 - DeepLabV3+ Training Resources: 4x V100 GPUS Memory (GB): 5.1 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes-20200825_124836.log.json Paper: Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks' URL: https://arxiv.org/abs/1801.04381 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14 Framework: PyTorch - Name: mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512 In Collection: FCN Results: Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 19.71 Config: configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512.py Metadata: Training Data: ADE20K Batch Size: 16 Architecture: - M-V2-D8 - FCN Training Resources: 4x V100 GPUS Memory (GB): 6.5 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k_20200825_214953-c40e1095.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k-20200825_214953.log.json Paper: Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks' URL: https://arxiv.org/abs/1801.04381 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14 Framework: PyTorch - Name: mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512 In Collection: PSPNet Results: Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 29.68 Config: configs/mobilenet_v2/mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512.py Metadata: Training Data: ADE20K Batch Size: 16 Architecture: - M-V2-D8 - PSPNet Training Resources: 4x V100 GPUS Memory (GB): 6.5 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k_20200825_214953-f5942f7a.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k-20200825_214953.log.json Paper: Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks' URL: https://arxiv.org/abs/1801.04381 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14 Framework: PyTorch - Name: mobilenet-v2-d8_deeplabv3_4xb4-160k_ade20k-512x512 In Collection: DeepLabV3 Results: Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 34.08 Config: configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3_4xb4-160k_ade20k-512x512.py Metadata: Training Data: ADE20K Batch Size: 16 Architecture: - M-V2-D8 - DeepLabV3 Training Resources: 4x V100 GPUS Memory (GB): 6.8 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k_20200825_223255-63986343.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k-20200825_223255.log.json Paper: Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks' URL: https://arxiv.org/abs/1801.04381 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14 Framework: PyTorch - Name: mobilenet-v2-d8_deeplabv3plus_4xb4-160k_ade20k-512x512 In Collection: DeepLabV3+ Results: Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 34.02 Config: configs/mobilenet_v2/mobilenet-v2-d8_deeplabv3plus_4xb4-160k_ade20k-512x512.py Metadata: Training Data: ADE20K Batch Size: 16 Architecture: - M-V2-D8 - DeepLabV3+ Training Resources: 4x V100 GPUS Memory (GB): 8.2 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k_20200825_223255-465a01d4.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k-20200825_223255.log.json Paper: Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks' URL: https://arxiv.org/abs/1801.04381 Code: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/backbones/mobilenet_v2.py#L14 Framework: PyTorch