mmpretrain/configs/maskfeat/metafile.yml

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YAML

Collections:
- Name: MaskFeat
Metadata:
Training Data: ImageNet-1k
Training Techniques:
- AdamW
Training Resources: 8x A100-80G GPUs
Architecture:
- ViT
Paper:
Title: Masked Feature Prediction for Self-Supervised Visual Pre-Training
URL: https://arxiv.org/abs/2112.09133v1
README: configs/maskfeat/README.md
Models:
- Name: maskfeat_vit-base-p16_8xb256-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/mmselfsup/1.x/maskfeat/maskfeat_vit-base-p16_8xb256-amp-coslr-300e_in1k/maskfeat_vit-base-p16_8xb256-amp-coslr-300e_in1k_20221101-6dfc8bf3.pth
Config: configs/maskfeat/maskfeat_vit-base-p16_8xb256-amp-coslr-300e_in1k.py
Downstream:
- vit-base-p16_maskfeat-pre_8xb256-coslr-100e_in1k
- Name: vit-base-p16_maskfeat-pre_8xb256-coslr-100e_in1k
Metadata:
Epochs: 100
Batch Size: 2048
FLOPs: 17581215744
Parameters: 86566120
Training Data: ImageNet-1k
In Collection: MaskFeat
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 83.4
Weights: https://download.openmmlab.com/mmselfsup/1.x/maskfeat/maskfeat_vit-base-p16_8xb256-amp-coslr-300e_in1k/vit-base-p16_ft-8xb256-coslr-100e_in1k/vit-base-p16_ft-8xb256-coslr-100e_in1k_20221028-5134431c.pth
Config: configs/maskfeat/benchmarks/vit-base-p16_8xb256-coslr-100e_in1k.py