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| 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 | |