89 lines
2.0 KiB
YAML
89 lines
2.0 KiB
YAML
Models:
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- Name: resnet50
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Results:
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- Dataset: ImageNet-1k
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Metrics:
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Top 1 Accuracy: 76.55
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Top 5 Accuracy: 93.06
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Config: configs/resnet/resnet50_8xb32_in1k.py
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Gpus: 8
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- Name: seresnet50
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Results:
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- Dataset: ImageNet-1k
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Metrics:
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Top 1 Accuracy: 77.74
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Top 5 Accuracy: 93.84
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Config: configs/seresnet/seresnet50_8xb32_in1k.py
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Gpus: 8
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- Name: vit-base
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Results:
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- Dataset: ImageNet-1k
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Metrics:
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Top 1 Accuracy: 82.37
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Top 5 Accuracy: 96.15
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Config: configs/vision_transformer/vit-base-p16_pt-32xb128-mae_in1k-224.py
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Gpus: 32
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- Name: mobilenetv2
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Results:
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- Dataset: ImageNet-1k
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Metrics:
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Top 1 Accuracy: 71.86
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Top 5 Accuracy: 90.42
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Config: configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py
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Gpus: 8
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- Name: swin_tiny
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Results:
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- Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 81.18
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Top 5 Accuracy: 95.61
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Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth
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Config: configs/swin_transformer/swin-tiny_16xb64_in1k.py
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Gpus: 16
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- Name: vgg16
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Results:
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- Dataset: ImageNet-1k
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Metrics:
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Top 1 Accuracy: 71.62
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Top 5 Accuracy: 90.49
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Config: configs/vgg/vgg16_8xb32_in1k.py
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Gpus: 8
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Months:
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- 1
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- 4
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- 7
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- 10
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- Name: shufflenet_v2
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Results:
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- Dataset: ImageNet-1k
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Metrics:
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Top 1 Accuracy: 69.55
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Top 5 Accuracy: 88.92
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Config: configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py
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Gpus: 16
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Months:
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- 2
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- 5
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- 8
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- 11
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- Name: resnet-rsb
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Results:
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- Dataset: ImageNet-1k
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Metrics:
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Top 1 Accuracy: 80.12
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Top 5 Accuracy: 94.78
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Config: configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py
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Gpus: 8
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Months:
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- 3
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- 6
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- 9
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- 12
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