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mmpretrain/configs/regnet/metafile.yml

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[Reproduction] Reproduce RegNetX training accuracy. (#587) * add preciseBN hook * add precise-bn config * add proority of preciseBN * Add precise BN hook * Update PreciseBN hook * add config example & pre-commit * Update PBN * Add unit test * Update unit tests * mv preciseBN to mmcls.runner * rm extra sleep() * merge latest * update code * fix lint * update unit tests * add regnetx configs * update prcise BN in configs * fix lint * add checkpoints and logs * fix conflict * fix lint * update model_zoo and model-index * remove some changes * update readme.md * remove precise_bn.py
2021-12-28 17:34:27 +08:00
Collections:
- Name: RegNet
Metadata:
Training Data: ImageNet-1k
Architecture:
- Neural Architecture Search
- Design Space Design
- Precise BN
- SGD with nesterov
Paper:
URL: https://arxiv.org/abs/2003.13678
Title: Designing Network Design Spaces
README: configs/regnet/README.md
Code:
[Docs] Update links (#1457) * update links * update readtherdocs * update * update * fix lint * update * update * update * update cov branch * update * update * update
2023-04-06 20:58:52 +08:00
URL: https://github.com/open-mmlab/mmpretrain/blob/v0.18.0/mmcls/models/backbones/regnet.py
[Reproduction] Reproduce RegNetX training accuracy. (#587) * add preciseBN hook * add precise-bn config * add proority of preciseBN * Add precise BN hook * Update PreciseBN hook * add config example & pre-commit * Update PBN * Add unit test * Update unit tests * mv preciseBN to mmcls.runner * rm extra sleep() * merge latest * update code * fix lint * update unit tests * add regnetx configs * update prcise BN in configs * fix lint * add checkpoints and logs * fix conflict * fix lint * update model_zoo and model-index * remove some changes * update readme.md * remove precise_bn.py
2021-12-28 17:34:27 +08:00
Version: v0.18.0
Models:
- Name: regnetx-400mf_8xb128_in1k
In Collection: RegNet
Config: configs/regnet/regnetx-400mf_8xb128_in1k.py
Metadata:
FLOPs: 410000000 # 0.41G
Parameters: 5160000 # 5.16M
Results:
- Dataset: ImageNet-1k
Task: Image Classification
Metrics:
Top 1 Accuracy: 72.56
Top 5 Accuracy: 90.78
Weights: https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211213-89bfc226.pth
- Name: regnetx-800mf_8xb128_in1k
In Collection: RegNet
Config: configs/regnet/regnetx-800mf_8xb128_in1k.py
Metadata:
FLOPs: 810000000 # 0.81G
Parameters: 7260000 # 7.26M
Results:
- Dataset: ImageNet-1k
Task: Image Classification
Metrics:
Top 1 Accuracy: 74.76
Top 5 Accuracy: 92.32
Weights: https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-800mf_8xb128_in1k_20211213-222b0f11.pth
- Name: regnetx-1.6gf_8xb128_in1k
In Collection: RegNet
Config: configs/regnet/regnetx-1.6gf_8xb128_in1k.py
Metadata:
FLOPs: 1630000000 # 1.63G
Parameters: 9190000 # 9.19M
Results:
- Dataset: ImageNet-1k
Task: Image Classification
Metrics:
Top 1 Accuracy: 76.84
Top 5 Accuracy: 93.31
Weights: https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-1.6gf_8xb128_in1k_20211213-d1b89758.pth
- Name: regnetx-3.2gf_8xb64_in1k
In Collection: RegNet
Config: configs/regnet/regnetx-3.2gf_8xb64_in1k.py
Metadata:
FLOPs: 1530000000 # 1.53G
Parameters: 3210000 # 32.1M
Results:
- Dataset: ImageNet-1k
Task: Image Classification
Metrics:
Top 1 Accuracy: 78.09
Top 5 Accuracy: 94.08
Weights: https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-3.2gf_8xb64_in1k_20211213-1fdd82ae.pth
- Name: regnetx-4.0gf_8xb64_in1k
In Collection: RegNet
Config: configs/regnet/regnetx-4.0gf_8xb64_in1k.py
Metadata:
FLOPs: 4000000000 # 4G
Parameters: 22120000 # 22.12M
Results:
- Dataset: ImageNet-1k
Task: Image Classification
Metrics:
Top 1 Accuracy: 78.60
Top 5 Accuracy: 94.17
Weights: https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-4.0gf_8xb64_in1k_20211213-efed675c.pth
- Name: regnetx-6.4gf_8xb64_in1k
In Collection: RegNet
Config: configs/regnet/regnetx-6.4gf_8xb64_in1k.py
Metadata:
FLOPs: 6510000000 # 6.51G
Parameters: 26210000 # 26.21M
Results:
- Dataset: ImageNet-1k
Task: Image Classification
Metrics:
Top 1 Accuracy: 79.38
Top 5 Accuracy: 94.65
Weights: https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-6.4gf_8xb64_in1k_20211215-5c6089da.pth
- Name: regnetx-8.0gf_8xb64_in1k
In Collection: RegNet
Config: configs/regnet/regnetx-8.0gf_8xb64_in1k.py
Metadata:
FLOPs: 8030000000 # 8.03G
Parameters: 39570000 # 39.57M
Results:
- Dataset: ImageNet-1k
Task: Image Classification
Metrics:
Top 1 Accuracy: 79.12
Top 5 Accuracy: 94.51
Weights: https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-8.0gf_8xb64_in1k_20211213-9a9fcc76.pth
- Name: regnetx-12gf_8xb64_in1k
In Collection: RegNet
Config: configs/regnet/regnetx-12gf_8xb64_in1k.py
Metadata:
FLOPs: 12150000000 # 12.15G
Parameters: 46110000 # 46.11M
Results:
- Dataset: ImageNet-1k
Task: Image Classification
Metrics:
Top 1 Accuracy: 79.67
Top 5 Accuracy: 95.03
Weights: https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-12gf_8xb64_in1k_20211213-5df8c2f8.pth