mmpretrain/configs/resnext/metafile.yml

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2.2 KiB
YAML

Collections:
- Name: ResNeXt
Metadata:
Training Data: ImageNet
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Epochs: 100
Batch Size: 256
Architecture:
- ResNeXt
Paper: https://openaccess.thecvf.com/content_cvpr_2017/html/Xie_Aggregated_Residual_Transformations_CVPR_2017_paper.html
README: configs/resnext/README.md
Models:
- Config: configs/resnext/resnext50_32x4d_b32x8_imagenet.py
In Collection: ResNeXt
Metadata:
FLOPs: 4270000000
Parameters: 25030000
Name: resnext50_32x4d_b32x8_imagenet
Results:
- Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.92
Top 5 Accuracy: 93.74
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_batch256_imagenet_20200708-c07adbb7.pth
- Config: configs/resnext/resnext101_32x4d_b32x8_imagenet.py
In Collection: ResNeXt
Metadata:
FLOPs: 8030000000
Parameters: 44180000
Name: resnext101_32x4d_b32x8_imagenet
Results:
- Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.7
Top 5 Accuracy: 94.34
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_batch256_imagenet_20200708-87f2d1c9.pth
- Config: configs/resnext/resnext101_32x8d_b32x8_imagenet.py
In Collection: ResNeXt
Metadata:
FLOPs: 16500000000
Parameters: 88790000
Name: resnext101_32x8d_b32x8_imagenet
Results:
- Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.22
Top 5 Accuracy: 94.52
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_batch256_imagenet_20200708-1ec34aa7.pth
- Config: configs/resnext/resnext152_32x4d_b32x8_imagenet.py
In Collection: ResNeXt
Metadata:
FLOPs: 11800000000
Parameters: 59950000
Name: resnext152_32x4d_b32x8_imagenet
Results:
- Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.06
Top 5 Accuracy: 94.47
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_batch256_imagenet_20200708-aab5034c.pth