Models: - Name: convnext-tiny_upernet_8xb2-amp-160k_ade20k-512x512 In Collection: UPerNet Results: Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 46.11 mIoU(ms+flip): 46.62 Config: configs/convnext/convnext-tiny_upernet_8xb2-amp-160k_ade20k-512x512.py Metadata: Training Data: ADE20K Batch Size: 16 Architecture: - ConvNeXt-T - UPerNet Training Resources: 8x V100 GPUS Memory (GB): 4.23 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553.log.json Paper: Title: A ConvNet for the 2020s URL: https://arxiv.org/abs/2201.03545 Code: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133 Framework: PyTorch - Name: convnext-small_upernet_8xb2-amp-160k_ade20k-512x512 In Collection: UPerNet Results: Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 48.56 mIoU(ms+flip): 49.02 Config: configs/convnext/convnext-small_upernet_8xb2-amp-160k_ade20k-512x512.py Metadata: Training Data: ADE20K Batch Size: 16 Architecture: - ConvNeXt-S - UPerNet Training Resources: 8x V100 GPUS Memory (GB): 5.16 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208.log.json Paper: Title: A ConvNet for the 2020s URL: https://arxiv.org/abs/2201.03545 Code: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133 Framework: PyTorch - Name: convnext-base_upernet_8xb2-amp-160k_ade20k-512x512 In Collection: UPerNet Results: Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 48.71 mIoU(ms+flip): 49.54 Config: configs/convnext/convnext-base_upernet_8xb2-amp-160k_ade20k-512x512.py Metadata: Training Data: ADE20K Batch Size: 16 Architecture: - ConvNeXt-B - UPerNet Training Resources: 8x V100 GPUS Memory (GB): 6.33 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227.log.json Paper: Title: A ConvNet for the 2020s URL: https://arxiv.org/abs/2201.03545 Code: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133 Framework: PyTorch - Name: convnext-base_upernet_8xb2-amp-160k_ade20k-640x640 In Collection: UPerNet Results: Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 52.13 mIoU(ms+flip): 52.66 Config: configs/convnext/convnext-base_upernet_8xb2-amp-160k_ade20k-640x640.py Metadata: Training Data: ADE20K Batch Size: 16 Architecture: - ConvNeXt-B - UPerNet Training Resources: 8x V100 GPUS Memory (GB): 8.53 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859-9280e39b.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859.log.json Paper: Title: A ConvNet for the 2020s URL: https://arxiv.org/abs/2201.03545 Code: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133 Framework: PyTorch - Name: convnext-large_upernet_8xb2-amp-160k_ade20k-640x640 In Collection: UPerNet Results: Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 53.16 mIoU(ms+flip): 53.38 Config: configs/convnext/convnext-large_upernet_8xb2-amp-160k_ade20k-640x640.py Metadata: Training Data: ADE20K Batch Size: 16 Architecture: - ConvNeXt-L - UPerNet Training Resources: 8x V100 GPUS Memory (GB): 12.08 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532.log.json Paper: Title: A ConvNet for the 2020s URL: https://arxiv.org/abs/2201.03545 Code: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133 Framework: PyTorch - Name: convnext-xlarge_upernet_8xb2-amp-160k_ade20k-640x640 In Collection: UPerNet Results: Task: Semantic Segmentation Dataset: ADE20K Metrics: mIoU: 53.58 mIoU(ms+flip): 54.11 Config: configs/convnext/convnext-xlarge_upernet_8xb2-amp-160k_ade20k-640x640.py Metadata: Training Data: ADE20K Batch Size: 16 Architecture: - ConvNeXt-XL - UPerNet Training Resources: 8x V100 GPUS Memory (GB): 26.16 Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth Training log: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344.log.json Paper: Title: A ConvNet for the 2020s URL: https://arxiv.org/abs/2201.03545 Code: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py#L133 Framework: PyTorch