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metafile.yml
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Collections:
- Name: MFF
Metadata:
Training Data: ImageNet-1k
Training Techniques:
- AdamW
Training Resources: 8x A100-80G GPUs
Architecture:
- ViT
Paper:
Title: Improving Pixel-based MIM by Reducing Wasted Modeling Capability
URL: https://arxiv.org/pdf/2308.00261.pdf
README: configs/mff/README.md
Models:
- Name: mff_vit-base-p16_8xb512-amp-coslr-300e_in1k
Metadata:
Epochs: 300
Batch Size: 2048
FLOPs: 17581972224
Parameters: 85882692
Training Data: ImageNet-1k
In Collection: MaskFeat
Results: null
Weights: https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k_20230801-3c1bcce4.pth
Config: configs/mff/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k.py
Downstream:
- vit-base-p16_mff-300e-pre_8xb128-coslr-100e_in1k
- vit-base-p16_mff-300e-pre_8xb2048-linear-coslr-90e_in1k
- Name: mff_vit-base-p16_8xb512-amp-coslr-800e_in1k
Metadata:
Epochs: 800
Batch Size: 2048
FLOPs: 17581972224
Parameters: 85882692
Training Data: ImageNet-1k
In Collection: MaskFeat
Results: null
Weights: https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-800e_in1k/mff_vit-base-p16_8xb512-amp-coslr-800e_in1k_20230801-3af7cd9d.pth
Config: configs/mff/mff_vit-base-p16_8xb512-amp-coslr-800e_in1k.py
Downstream:
- vit-base-p16_mff-800e-pre_8xb128-coslr-100e_in1k
- vit-base-p16_mff-800e-pre_8xb2048-linear-coslr-90e_in1k
- Name: vit-base-p16_mff-300e-pre_8xb128-coslr-100e_in1k
Metadata:
Epochs: 100
Batch Size: 1024
FLOPs: 17581215744
Parameters: 86566120
Training Data: ImageNet-1k
In Collection: MaskFeat
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 83.0
Weights: https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k/vit-base-p16_8xb128-coslr-100e_in1k/vit-base-p16_8xb128-coslr-100e_in1k_20230802-d746fdb7.pth
Config: configs/mff/benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py
- Name: vit-base-p16_mff-800e-pre_8xb128-coslr-100e_in1k
Metadata:
Epochs: 100
Batch Size: 1024
FLOPs: 17581215744
Parameters: 86566120
Training Data: ImageNet-1k
In Collection: MFF
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 83.7
Weights: https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-800e_in1k/vit-base-p16_8xb128-coslr-100e/vit-base-p16_8xb128-coslr-100e_20230802-6780e47d.pth
Config: configs/mff/benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py
- Name: vit-base-p16_mff-300e-pre_8xb2048-linear-coslr-90e_in1k
Metadata:
Epochs: 90
Batch Size: 16384
FLOPs: 17581215744
Parameters: 86566120
Training Data: ImageNet-1k
In Collection: MFF
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 64.2
Weights:
Config: configs/mff/benchmarks/vit-base-p16_8xb2048-linear-coslr-90e_in1k.py
- Name: vit-base-p16_mff-800e-pre_8xb2048-linear-coslr-90e_in1k
Metadata:
Epochs: 90
Batch Size: 16384
FLOPs: 17581215744
Parameters: 86566120
Training Data: ImageNet-1k
In Collection: MFF
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 68.3
Weights: https://download.openmmlab.com/mmpretrain/v1.0/mff/mff_vit-base-p16_8xb512-amp-coslr-300e_in1k/vit-base-p16_8xb128-coslr-100e_in1k/vit-base-p16_8xb128-coslr-100e_in1k_20230802-d746fdb7.pth
Config: configs/mff/benchmarks/vit-base-p16_8xb2048-linear-coslr-90e_in1k.py