[Feature] Adding PWC metafile (#225)
* add yml * fix VGG * minor fix * add model zoo * add bs and epoch * revised according to comments * revised according to comments * revised according to commentspull/229/head
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Collections:
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- Name: FP16
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Metadata:
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Training Data: ImageNet
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Training Resources: 8x V100 GPUs
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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- Mixed Precision Training
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Paper: https://arxiv.org/abs/1710.03740
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README: configs/fp16/README.md
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Models:
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- Config: configs/fp16/resnet50_b32x8_fp16_dynamic_imagenet.py
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In Collection: FP16
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Metadata:
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FLOPs: 4120000000
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Parameters: 25560000
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Epochs: 100
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Batch Size: 256
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Architecture:
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- ResNet
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Name: resnet50_b32x8_fp16_dynamic_imagenet
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Results:
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- Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 76.32
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Top 5 Accuracy: 93.04
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.pth
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Collections:
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- Name: MobileNet V2
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Metadata:
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Training Data: ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x V100 GPUs
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Epochs: 300
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Batch Size: 256
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Architecture:
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- MobileNet V2
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Paper: https://arxiv.org/abs/1801.04381
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README: configs/mobilenet_v2/README.md
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Models:
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- Config: configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py
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In Collection: MobileNet V2
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Metadata:
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FLOPs: 319000000
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Parameters: 3500000
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Name: mobilenet_v2_b32x8_imagenet
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Results:
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- Dataset: ImageNet
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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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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth
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Collections:
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- Name: ResNet
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Metadata:
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Training Data: ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x V100 GPUs
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Epochs: 100
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Batch Size: 256
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Architecture:
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- ResNet
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Paper: https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html
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README: configs/resnet/README.md
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Models:
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- Config: configs/resnet/resnet18_b16x8_cifar10.py
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In Collection: ResNet
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Metadata:
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FLOPs: 560000000
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Parameters: 11170000
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Training Data: CIFAR-10
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Training Resources: 8x 1080 GPUs
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Epochs: 200
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Batch Size: 128
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Name: resnet18_b16x8_cifar10
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Results:
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- Dataset: CIFAR-10
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Metrics:
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Top 1 Accuracy: 94.72
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20200823-f906fa4e.pth
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- Config: configs/resnet/resnet34_b16x8_cifar10.py
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In Collection: ResNet
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Metadata:
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FLOPs: 1160000000
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Parameters: 21280000
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Training Data: CIFAR-10
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Training Resources: 8x 1080 GPUs
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Epochs: 200
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Batch Size: 128
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Name: resnet34_b16x8_cifar10
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Results:
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- Dataset: CIFAR-10
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Metrics:
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Top 1 Accuracy: 95.34
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20200823-52d5d832.pth
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- Config: configs/resnet/resnet50_b16x8_cifar10.py
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In Collection: ResNet
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Metadata:
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FLOPs: 1310000000
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Parameters: 23520000
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Training Data: CIFAR-10
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Training Resources: 8x 1080 GPUs
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Epochs: 200
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Batch Size: 128
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Name: resnet50_b16x8_cifar10
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Results:
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- Dataset: CIFAR-10
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Metrics:
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Top 1 Accuracy: 95.36
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20200823-882aa7b1.pth
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- Config: configs/resnet/resnet101_b16x8_cifar10.py
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In Collection: ResNet
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Metadata:
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FLOPs: 2520000000
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Parameters: 42510000
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Training Data: CIFAR-10
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Training Resources: 8x 1080 GPUs
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Epochs: 200
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Batch Size: 128
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Name: resnet101_b16x8_cifar10
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Results:
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- Dataset: CIFAR-10
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Metrics:
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Top 1 Accuracy: 95.66
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20200823-d9501bbc.pth
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- Config: configs/resnet/resnet152_b16x8_cifar10.py
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In Collection: ResNet
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Metadata:
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FLOPs: 3740000000
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Parameters: 58160000
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Training Data: CIFAR-10
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Training Resources: 8x 1080 GPUs
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Epochs: 200
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Batch Size: 128
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Name: resnet152_b16x8_cifar10
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Results:
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- Dataset: CIFAR-10
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Metrics:
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Top 1 Accuracy: 95.96
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20200823-ad4d5d0c.pth
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- Config: configs/resnet/resnet50_b16x8_cifar100.py
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In Collection: ResNet
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Metadata:
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FLOPs: 1310000000
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Parameters: 23710000
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Training Data: CIFAR-100
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Training Resources: 8x 1080 GPUs
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Epochs: 200
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Batch Size: 128
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Name: resnet50_b16x8_cifar100
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Results:
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- Dataset: CIFAR-100
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Metrics:
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Top 1 Accuracy: 80.51
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Top 5 Accuracy: 95.27
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_cifar100_20210410-37f13c16.pth
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- Config: configs/resnet/resnet18_b32x8_imagenet.py
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In Collection: ResNet
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Metadata:
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FLOPs: 1820000000
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Parameters: 11690000
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Name: resnet18_b32x8_imagenet
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Results:
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- Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 70.07
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Top 5 Accuracy: 89.44
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.pth
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- Config: configs/resnet/resnet34_b32x8_imagenet.py
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In Collection: ResNet
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Metadata:
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FLOPs: 3680000000
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Parameters: 2180000
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Name: resnet34_b32x8_imagenet
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Results:
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- Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 73.85
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Top 5 Accuracy: 91.53
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.pth
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- Config: configs/resnet/resnet50_b32x8_imagenet.py
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In Collection: ResNet
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Metadata:
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FLOPs: 4120000000
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Parameters: 25560000
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Name: resnet50_b32x8_imagenet
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Results:
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- Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 76.55
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Top 5 Accuracy: 93.15
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth
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- Config: configs/resnet/resnet101_b32x8_imagenet.py
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In Collection: ResNet
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Metadata:
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FLOPs: 7850000000
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Parameters: 44550000
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Name: resnet101_b32x8_imagenet
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Results:
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- Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 78.18
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Top 5 Accuracy: 94.03
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.pth
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- Config: configs/resnet/resnet152_b32x8_imagenet.py
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In Collection: ResNet
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Metadata:
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FLOPs: 11580000000
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Parameters: 60190000
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Name: resnet152_b32x8_imagenet
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Results:
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- Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 78.63
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Top 5 Accuracy: 94.16
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.pth
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- Config: configs/resnet/resnetv1d50_b32x8_imagenet.py
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In Collection: ResNet
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Metadata:
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FLOPs: 4360000000
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Parameters: 25580000
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Name: resnetv1d50_b32x8_imagenet
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Results:
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- Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 77.4
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Top 5 Accuracy: 93.66
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_batch256_imagenet_20200708-1ad0ce94.pth
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- Config: configs/resnet/resnetv1d101_b32x8_imagenet.py
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In Collection: ResNet
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Metadata:
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FLOPs: 8090000000
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Parameters: 44570000
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Name: resnetv1d101_b32x8_imagenet
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Results:
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- Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 78.85
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Top 5 Accuracy: 94.38
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_batch256_imagenet_20200708-9cb302ef.pth
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- Config: configs/resnet/resnetv1d152_b32x8_imagenet.py
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In Collection: ResNet
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Metadata:
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FLOPs: 11820000000
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Parameters: 60210000
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Name: resnetv1d152_b32x8_imagenet
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Results:
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- Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 79.35
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Top 5 Accuracy: 94.61
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_batch256_imagenet_20200708-e79cb6a2.pth
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Collections:
|
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- Name: ResNeXt
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Metadata:
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Training Data: ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x V100 GPUs
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Epochs: 100
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Batch Size: 256
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Architecture:
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- ResNeXt
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Paper: https://openaccess.thecvf.com/content_cvpr_2017/html/Xie_Aggregated_Residual_Transformations_CVPR_2017_paper.html
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README: configs/resnext/README.md
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Models:
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- Config: configs/resnext/resnext50_32x4d_b32x8_imagenet.py
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In Collection: ResNeXt
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Metadata:
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FLOPs: 4270000000
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Parameters: 25030000
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Name: resnext50_32x4d_b32x8_imagenet
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Results:
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- Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 77.92
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Top 5 Accuracy: 93.74
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_batch256_imagenet_20200708-c07adbb7.pth
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- Config: configs/resnext/resnext101_32x4d_b32x8_imagenet.py
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In Collection: ResNeXt
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Metadata:
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FLOPs: 8030000000
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Parameters: 44180000
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Name: resnext101_32x4d_b32x8_imagenet
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Results:
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- Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 78.7
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Top 5 Accuracy: 94.34
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_batch256_imagenet_20200708-87f2d1c9.pth
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- Config: configs/resnext/resnext101_32x8d_b32x8_imagenet.py
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In Collection: ResNeXt
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Metadata:
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FLOPs: 16500000000
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Parameters: 88790000
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Name: resnext101_32x8d_b32x8_imagenet
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Results:
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- Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 79.22
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Top 5 Accuracy: 94.52
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_batch256_imagenet_20200708-1ec34aa7.pth
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- Config: configs/resnext/resnext152_32x4d_b32x8_imagenet.py
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In Collection: ResNeXt
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Metadata:
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FLOPs: 11800000000
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Parameters: 59950000
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Name: resnext152_32x4d_b32x8_imagenet
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Results:
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- Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 79.06
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Top 5 Accuracy: 94.47
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_batch256_imagenet_20200708-aab5034c.pth
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Collections:
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- Name: SEResNet
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Metadata:
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Training Data: ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x V100 GPUs
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Epochs: 140
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Batch Size: 256
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Architecture:
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- ResNet
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Paper: https://openaccess.thecvf.com/content_cvpr_2018/html/Hu_Squeeze-and-Excitation_Networks_CVPR_2018_paper.html
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README: configs/seresnet/README.md
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Models:
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- Config: configs/seresnet50/seresnet50_b32x8_imagenet.py
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In Collection: SEResNet
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Metadata:
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FLOPs: 4130000000
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Parameters: 28090000
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Name: seresnet50_b32x8_imagenet
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Results:
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- Dataset: ImageNet
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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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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth
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- Config: configs/seresnet101/seresnet101_b32x8_imagenet.py
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In Collection: SEResNet
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Metadata:
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FLOPs: 7860000000
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Parameters: 49330000
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Name: seresnet101_b32x8_imagenet
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Results:
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- Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 78.26
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Top 5 Accuracy: 94.07
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth
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Collections:
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- Name: Shufflenet V1
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Metadata:
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Training Data: ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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- No BN decay
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Training Resources: 8x 1080 GPUs
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Epochs: 300
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Batch Size: 1024
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Architecture:
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- Shufflenet V1
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Paper: https://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_ShuffleNet_An_Extremely_CVPR_2018_paper.html
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README: configs/shufflenet_v1/README.md
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Models:
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- Config: configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py
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In Collection: Shufflenet V1
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Metadata:
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FLOPs: 146000000
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Parameters: 1870000
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Name: shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet
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Results:
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- Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 68.13
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Top 5 Accuracy: 87.81
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Task: Image Classification
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Weights: https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth
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Collections:
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- Name: Shufflenet V2
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Metadata:
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Training Data: ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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- No BN decay
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Training Resources: 8x 1080 GPUs
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Epochs: 300
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Batch Size: 1024
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Architecture:
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- Shufflenet V2
|
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Paper: https://openaccess.thecvf.com/content_ECCV_2018/papers/Ningning_Light-weight_CNN_Architecture_ECCV_2018_paper.pdf
|
||||
README: configs/shufflenet_v2/README.md
|
||||
|
||||
Models:
|
||||
- Config: configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py
|
||||
In Collection: Shufflenet V2
|
||||
Metadata:
|
||||
FLOPs: 149000000
|
||||
Parameters: 2280000
|
||||
Name: shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet
|
||||
Results:
|
||||
- Dataset: ImageNet
|
||||
Metrics:
|
||||
Top 1 Accuracy: 69.55
|
||||
Top 5 Accuracy: 88.92
|
||||
Task: Image Classification
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth
|
||||
|
|
@ -20,11 +20,11 @@
|
|||
|
||||
| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
|
||||
|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
|
||||
| VGG-11 | 132.86 | 7.63 | 69.03 | 88.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_imagenet-01ecd97e.pth)* |
|
||||
| VGG-13 | 133.05 | 11.34 | 69.93 | 89.26 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_imagenet-9ad3945d.pth)*|
|
||||
| VGG-16 | 138.36 | 15.5 | 71.59 | 90.39 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_imagenet-91b6d117.pth)*|
|
||||
| VGG-19 | 143.67 | 19.67 | 72.38 | 90.88 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_imagenet-fee352a8.pth)*|
|
||||
| VGG-11-BN | 132.87 | 7.64 | 70.37 | 89.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11bn_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_imagenet-6fbbbf3f.pth)*|
|
||||
| VGG-13-BN | 133.05 | 11.36 | 71.55 | 90.37 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13bn_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_imagenet-4b5f9390.pth)*|
|
||||
| VGG-16-BN | 138.37 | 15.53 | 73.36 | 91.5 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_imagenet-3ac6d8fd.pth)*|
|
||||
| VGG-19-BN | 143.68 | 19.7 | 74.24 | 91.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19bn_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_imagenet-7c058385.pth)*|
|
||||
| VGG-11 | 132.86 | 7.63 | 68.75 | 88.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.log.json) |
|
||||
| VGG-13 | 133.05 | 11.34 | 70.02 | 89.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.log.json) |
|
||||
| VGG-16 | 138.36 | 15.5 | 71.62 | 90.49 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.log.json) |
|
||||
| VGG-19 | 143.67 | 19.67 | 72.41 | 90.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.log.json)|
|
||||
| VGG-11-BN | 132.87 | 7.64 | 70.75 | 90.12 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11bn_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.log.json) |
|
||||
| VGG-13-BN | 133.05 | 11.36 | 72.15 | 90.71 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13bn_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.log.json) |
|
||||
| VGG-16-BN | 138.37 | 15.53 | 73.72 | 91.68 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.log.json) |
|
||||
| VGG-19-BN | 143.68 | 19.7 | 74.70 | 92.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19bn_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.log.json)|
|
||||
|
|
|
|||
|
|
@ -0,0 +1,120 @@
|
|||
Collections:
|
||||
- Name: VGG
|
||||
Metadata:
|
||||
Training Data: ImageNet
|
||||
Training Techniques:
|
||||
- SGD with Momentum
|
||||
- Weight Decay
|
||||
Training Resources: 8x Xp GPUs
|
||||
Epochs: 100
|
||||
Batch Size: 256
|
||||
Architecture:
|
||||
- VGG
|
||||
Paper: https://arxiv.org/abs/1409.1556
|
||||
README: configs/vgg/README.md
|
||||
|
||||
Models:
|
||||
- Config: configs/vgg/vgg11_b32x8_imagenet.py
|
||||
In Collection: VGG
|
||||
Metadata:
|
||||
FLOPs: 7630000000
|
||||
Parameters: 132860000
|
||||
Name: vgg11_b32x8_imagenet
|
||||
Results:
|
||||
- Dataset: ImageNet
|
||||
Metrics:
|
||||
Top 1 Accuracy: 68.75
|
||||
Top 5 Accuracy: 88.87
|
||||
Task: Image Classification
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth
|
||||
- Config: configs/vgg/vgg13_b32x8_imagenet.py
|
||||
In Collection: VGG
|
||||
Metadata:
|
||||
FLOPs: 11340000000
|
||||
Parameters: 133050000
|
||||
Name: vgg13_b32x8_imagenet
|
||||
Results:
|
||||
- Dataset: ImageNet
|
||||
Metrics:
|
||||
Top 1 Accuracy: 70.02
|
||||
Top 5 Accuracy: 89.46
|
||||
Task: Image Classification
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth
|
||||
- Config: configs/vgg/vgg16_b32x8_imagenet.py
|
||||
In Collection: VGG
|
||||
Metadata:
|
||||
FLOPs: 15500000000
|
||||
Parameters: 138360000
|
||||
Name: vgg16_b32x8_imagenet
|
||||
Results:
|
||||
- Dataset: ImageNet
|
||||
Metrics:
|
||||
Top 1 Accuracy: 71.62
|
||||
Top 5 Accuracy: 90.49
|
||||
Task: Image Classification
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth
|
||||
- Config: configs/vgg/vgg19_b32x8_imagenet.py
|
||||
In Collection: VGG
|
||||
Metadata:
|
||||
FLOPs: 19670000000
|
||||
Parameters: 143670000
|
||||
Name: vgg19_b32x8_imagenet
|
||||
Results:
|
||||
- Dataset: ImageNet
|
||||
Metrics:
|
||||
Top 1 Accuracy: 72.41
|
||||
Top 5 Accuracy: 90.8
|
||||
Task: Image Classification
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth
|
||||
- Config: configs/vgg/vgg11bn_b32x8_imagenet.py
|
||||
In Collection: VGG
|
||||
Metadata:
|
||||
FLOPs: 7640000000
|
||||
Parameters: 132870000
|
||||
Name: vgg11bn_b32x8_imagenet
|
||||
Results:
|
||||
- Dataset: ImageNet
|
||||
Metrics:
|
||||
Top 1 Accuracy: 70.75
|
||||
Top 5 Accuracy: 90.12
|
||||
Task: Image Classification
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth
|
||||
- Config: configs/vgg/vgg13bn_b32x8_imagenet.py
|
||||
In Collection: VGG
|
||||
Metadata:
|
||||
FLOPs: 11360000000
|
||||
Parameters: 133050000
|
||||
Name: vgg13bn_b32x8_imagenet
|
||||
Results:
|
||||
- Dataset: ImageNet
|
||||
Metrics:
|
||||
Top 1 Accuracy: 72.15
|
||||
Top 5 Accuracy: 90.71
|
||||
Task: Image Classification
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth
|
||||
- Config: configs/vgg/vgg16_b32x8_imagenet.py
|
||||
In Collection: VGG
|
||||
Metadata:
|
||||
FLOPs: 15530000000
|
||||
Parameters: 138370000
|
||||
Name: vgg16_b32x8_imagenet
|
||||
Results:
|
||||
- Dataset: ImageNet
|
||||
Metrics:
|
||||
Top 1 Accuracy: 73.72
|
||||
Top 5 Accuracy: 91.68
|
||||
Task: Image Classification
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth
|
||||
- Config: configs/vgg/vgg19bn_b32x8_imagenet.py
|
||||
In Collection: VGG
|
||||
Metadata:
|
||||
FLOPs: 19700000000
|
||||
Parameters: 143680000
|
||||
Name: vgg19bn_b32x8_imagenet
|
||||
Results:
|
||||
- Dataset: ImageNet
|
||||
Metrics:
|
||||
Top 1 Accuracy: 74.7
|
||||
Top 5 Accuracy: 92.24
|
||||
Task: Image Classification
|
||||
Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
Import:
|
||||
- configs/fp16/metafile.yml
|
||||
- configs/mobilenet_v2/metafile.yml
|
||||
- configs/resnet/metafile.yml
|
||||
- configs/resnext/metafile.yml
|
||||
- configs/seresnet/metafile.yml
|
||||
- configs/seresnext/metafile.yml
|
||||
- configs/shufflenet_v1/metafile.yml
|
||||
- configs/shufflenet_v2/metafile.yml
|
||||
- configs/vgg/metafile.yml
|
||||
Loading…
Reference in New Issue