mirrors
/
mmpretrain
mirror of https://github.com/open-mmlab/mmpretrain.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
mmpretrain/configs/mae/metafile.yml

368 lines
13 KiB
YAML
Raw Blame History

Collections:
- Name: MAE
Metadata:
Training Data: ImageNet-1k
Training Techniques:
- AdamW
Training Resources: 8x A100-80G GPUs
Architecture:
- ViT
Paper:
Title: Masked Autoencoders Are Scalable Vision Learners
URL: https://arxiv.org/abs/2111.06377
README: configs/mae/README.md
Models:
- Name: mae_vit-base-p16_8xb512-amp-coslr-300e_in1k
Metadata:
Epochs: 300
Batch Size: 4096
FLOPs: 17581972224
Parameters: 111907840
Training Data: ImageNet-1k
In Collection: MAE
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-base-p16_8xb512-fp16-coslr-300e_in1k/mae_vit-base-p16_8xb512-coslr-300e-fp16_in1k_20220829-c2cf66ba.pth
Config: configs/mae/mae_vit-base-p16_8xb512-amp-coslr-300e_in1k.py
Downstream:
- vit-base-p16_mae-300e-pre_8xb2048-linear-coslr-90e_in1k
- vit-base-p16_mae-300e-pre_8xb128-coslr-100e_in1k
- Name: mae_vit-base-p16_8xb512-amp-coslr-400e_in1k
Metadata:
Epochs: 400
Batch Size: 4096
FLOPs: 17581972224
Parameters: 111907840
Training Data: ImageNet-1k
In Collection: MAE
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-base-p16_8xb512-fp16-coslr-400e_in1k/mae_vit-base-p16_8xb512-coslr-400e-fp16_in1k_20220825-bc79e40b.pth
Config: configs/mae/mae_vit-base-p16_8xb512-amp-coslr-400e_in1k.py
Downstream:
- vit-base-p16_mae-400e-pre_8xb2048-linear-coslr-90e_in1k
- vit-base-p16_mae-400e-pre_8xb128-coslr-100e_in1k
- Name: mae_vit-base-p16_8xb512-amp-coslr-800e_in1k
Metadata:
Epochs: 800
Batch Size: 4096
FLOPs: 17581972224
Parameters: 111907840
Training Data: ImageNet-1k
In Collection: MAE
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-base-p16_8xb512-fp16-coslr-800e_in1k/mae_vit-base-p16_8xb512-coslr-800e-fp16_in1k_20220825-5d81fbc4.pth
Config: configs/mae/mae_vit-base-p16_8xb512-amp-coslr-800e_in1k.py
Downstream:
- vit-base-p16_mae-800e-pre_8xb2048-linear-coslr-90e_in1k
- vit-base-p16_mae-800e-pre_8xb128-coslr-100e_in1k
- Name: mae_vit-base-p16_8xb512-amp-coslr-1600e_in1k
Metadata:
Epochs: 1600
Batch Size: 4096
FLOPs: 17581972224
Parameters: 111907840
Training Data: ImageNet-1k
In Collection: MAE
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-base-p16_8xb512-fp16-coslr-1600e_in1k/mae_vit-base-p16_8xb512-fp16-coslr-1600e_in1k_20220825-f7569ca2.pth
Config: configs/mae/mae_vit-base-p16_8xb512-amp-coslr-1600e_in1k.py
Downstream:
- vit-base-p16_mae-1600e-pre_8xb2048-linear-coslr-90e_in1k
- vit-base-p16_mae-1600e-pre_8xb128-coslr-100e_in1k
- Name: mae_vit-large-p16_8xb512-amp-coslr-400e_in1k
Metadata:
Epochs: 400
Batch Size: 4096
FLOPs: 61603111936
Parameters: 329541888
Training Data: ImageNet-1k
In Collection: MAE
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-large-p16_8xb512-fp16-coslr-400e_in1k/mae_vit-large-p16_8xb512-fp16-coslr-400e_in1k_20220825-b11d0425.pth
Config: configs/mae/mae_vit-large-p16_8xb512-amp-coslr-400e_in1k.py
Downstream:
- vit-large-p16_mae-400e-pre_8xb2048-linear-coslr-90e_in1k
- vit-large-p16_mae-400e-pre_8xb128-coslr-50e_in1k
- Name: mae_vit-large-p16_8xb512-amp-coslr-800e_in1k
Metadata:
Epochs: 800
Batch Size: 4096
FLOPs: 61603111936
Parameters: 329541888
Training Data: ImageNet-1k
In Collection: MAE
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-large-p16_8xb512-fp16-coslr-800e_in1k/mae_vit-large-p16_8xb512-fp16-coslr-800e_in1k_20220825-df72726a.pth
Config: configs/mae/mae_vit-large-p16_8xb512-amp-coslr-800e_in1k.py
Downstream:
- vit-large-p16_mae-800e-pre_8xb2048-linear-coslr-90e_in1k
- vit-large-p16_mae-800e-pre_8xb128-coslr-50e_in1k
- Name: mae_vit-large-p16_8xb512-amp-coslr-1600e_in1k
Metadata:
Epochs: 1600
Batch Size: 4096
FLOPs: 61603111936
Parameters: 329541888
Training Data: ImageNet-1k
In Collection: MAE
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-large-p16_8xb512-fp16-coslr-1600e_in1k/mae_vit-large-p16_8xb512-fp16-coslr-1600e_in1k_20220825-cc7e98c9.pth
Config: configs/mae/mae_vit-large-p16_8xb512-amp-coslr-1600e_in1k.py
Downstream:
- vit-large-p16_mae-1600e-pre_8xb2048-linear-coslr-90e_in1k
- vit-large-p16_mae-1600e-pre_8xb128-coslr-50e_in1k
- Name: mae_vit-huge-p16_8xb512-amp-coslr-1600e_in1k
Metadata:
Epochs: 1600
Batch Size: 4096
FLOPs: 167400741120
Parameters: 657074508
Training Data: ImageNet-1k
In Collection: MAE
Results: null
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-huge-p16_8xb512-fp16-coslr-1600e_in1k/mae_vit-huge-p16_8xb512-fp16-coslr-1600e_in1k_20220916-ff848775.pth
Config: configs/mae/mae_vit-huge-p14_8xb512-amp-coslr-1600e_in1k.py
Downstream:
- vit-huge-p14_mae-1600e-pre_8xb128-coslr-50e_in1k
- vit-huge-p14_mae-1600e-pre_32xb8-coslr-50e_in1k-448px
- Name: vit-base-p16_mae-300e-pre_8xb128-coslr-100e_in1k
Metadata:
Epochs: 100
Batch Size: 1024
FLOPs: 17581215744
Parameters: 86566120
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 83.1
Weights: null
Config: configs/mae/benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py
- Name: vit-base-p16_mae-400e-pre_8xb128-coslr-100e_in1k
Metadata:
Epochs: 100
Batch Size: 1024
FLOPs: 17581215744
Parameters: 86566120
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 83.3
Weights: null
Config: configs/mae/benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py
- Name: vit-base-p16_mae-800e-pre_8xb128-coslr-100e_in1k
Metadata:
Epochs: 100
Batch Size: 1024
FLOPs: 17581215744
Parameters: 86566120
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 83.3
Weights: null
Config: configs/mae/benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py
- Name: vit-base-p16_mae-1600e-pre_8xb128-coslr-100e_in1k
Metadata:
Epochs: 100
Batch Size: 1024
FLOPs: 17581215744
Parameters: 86566120
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 83.5
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-base-p16_8xb512-fp16-coslr-1600e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k/vit-base-p16_ft-8xb128-coslr-100e_in1k_20220825-cf70aa21.pth
Config: configs/mae/benchmarks/vit-base-p16_8xb128-coslr-100e_in1k.py
- Name: vit-base-p16_mae-300e-pre_8xb2048-linear-coslr-90e_in1k
Metadata:
Epochs: 90
Batch Size: 16384
FLOPs: 17581972992
Parameters: 86567656
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 60.8
Weights: null
Config: configs/mae/benchmarks/vit-base-p16_8xb2048-linear-coslr-90e_in1k.py
- Name: vit-base-p16_mae-400e-pre_8xb2048-linear-coslr-90e_in1k
Metadata:
Epochs: 90
Batch Size: 16384
FLOPs: 17581972992
Parameters: 86567656
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 62.5
Weights: null
Config: configs/mae/benchmarks/vit-base-p16_8xb2048-linear-coslr-90e_in1k.py
- Name: vit-base-p16_mae-800e-pre_8xb2048-linear-coslr-90e_in1k
Metadata:
Epochs: 90
Batch Size: 16384
FLOPs: 17581972992
Parameters: 86567656
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 65.1
Weights: null
Config: configs/mae/benchmarks/vit-base-p16_8xb2048-linear-coslr-90e_in1k.py
- Name: vit-base-p16_mae-1600e-pre_8xb2048-linear-coslr-90e_in1k
Metadata:
Epochs: 90
Batch Size: 16384
FLOPs: 17581972992
Parameters: 86567656
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 67.1
Weights: null
Config: configs/mae/benchmarks/vit-base-p16_8xb2048-linear-coslr-90e_in1k.py
- Name: vit-large-p16_mae-400e-pre_8xb128-coslr-50e_in1k
Metadata:
Epochs: 50
Batch Size: 1024
FLOPs: 61602103296
Parameters: 304324584
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 85.2
Weights: null
Config: configs/mae/benchmarks/vit-large-p16_8xb128-coslr-50e_in1k.py
- Name: vit-large-p16_mae-800e-pre_8xb128-coslr-50e_in1k
Metadata:
Epochs: 50
Batch Size: 1024
FLOPs: 61602103296
Parameters: 304324584
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 85.4
Weights: null
Config: configs/mae/benchmarks/vit-large-p16_8xb128-coslr-50e_in1k.py
- Name: vit-large-p16_mae-1600e-pre_8xb128-coslr-50e_in1k
Metadata:
Epochs: 50
Batch Size: 1024
FLOPs: 61602103296
Parameters: 304324584
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 85.7
Weights: null
Config: configs/mae/benchmarks/vit-large-p16_8xb128-coslr-50e_in1k.py
- Name: vit-large-p16_mae-400e-pre_8xb2048-linear-coslr-90e_in1k
Metadata:
Epochs: 90
Batch Size: 16384
FLOPs: 61603112960
Parameters: 304326632
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 70.7
Weights: null
Config: configs/mae/benchmarks/vit-large-p16_8xb2048-linear-coslr-90e_in1k.py
- Name: vit-large-p16_mae-800e-pre_8xb2048-linear-coslr-90e_in1k
Metadata:
Epochs: 90
Batch Size: 16384
FLOPs: 61603112960
Parameters: 304326632
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 73.7
Weights: null
Config: configs/mae/benchmarks/vit-large-p16_8xb2048-linear-coslr-90e_in1k.py
- Name: vit-large-p16_mae-1600e-pre_8xb2048-linear-coslr-90e_in1k
Metadata:
Epochs: 90
Batch Size: 16384
FLOPs: 61603112960
Parameters: 304326632
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 75.5
Weights: null
Config: configs/mae/benchmarks/vit-large-p16_8xb2048-linear-coslr-90e_in1k.py
- Name: vit-huge-p14_mae-1600e-pre_8xb128-coslr-50e_in1k
Metadata:
Epochs: 50
Batch Size: 1024
FLOPs: 167399096320
Parameters: 632043240
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 86.9
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-huge-p16_8xb512-fp16-coslr-1600e_in1k/vit-huge-p16_ft-8xb128-coslr-50e_in1k/vit-huge-p16_ft-8xb128-coslr-50e_in1k_20220916-0bfc9bfd.pth
Config: configs/mae/benchmarks/vit-huge-p14_8xb128-coslr-50e_in1k.py
- Name: vit-huge-p14_mae-1600e-pre_32xb8-coslr-50e_in1k-448px
Metadata:
Epochs: 50
Batch Size: 256
FLOPs: 732131983360
Parameters: 633026280
Training Data: ImageNet-1k
In Collection: MAE
Results:
- Task: Image Classification
Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 87.3
Weights: https://download.openmmlab.com/mmselfsup/1.x/mae/mae_vit-huge-p16_8xb512-fp16-coslr-1600e_in1k/vit-huge-p16_ft-32xb8-coslr-50e_in1k-448/vit-huge-p16_ft-32xb8-coslr-50e_in1k-448_20220916-95b6a0ce.pth
Config: configs/mae/benchmarks/vit-huge-p14_32xb8-coslr-50e_in1k-448px.py
Reference in New Issue View Git Blame Copy Permalink