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: URL: https://github.com/open-mmlab/mmpretrain/blob/v0.18.0/mmcls/models/backbones/regnet.py 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