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256 lines
6.5 KiB
256 lines
6.5 KiB
4 years ago
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# Summary
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A **TResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that aim to boost accuracy while maintaining GPU training and inference efficiency. They contain several design tricks including a SpaceToDepth stem, [Anti-Alias downsampling](https://paperswithcode.com/method/anti-alias-downsampling), In-Place Activated BatchNorm, Blocks selection and [squeeze-and-excitation layers](https://paperswithcode.com/method/squeeze-and-excitation-block).
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{% include 'code_snippets.md' %}
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## How do I train this model?
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You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
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## Citation
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```BibTeX
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@misc{ridnik2020tresnet,
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title={TResNet: High Performance GPU-Dedicated Architecture},
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author={Tal Ridnik and Hussam Lawen and Asaf Noy and Emanuel Ben Baruch and Gilad Sharir and Itamar Friedman},
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year={2020},
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eprint={2003.13630},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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<!--
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Models:
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- Name: tresnet_l
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Metadata:
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FLOPs: 10873416792
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Epochs: 300
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Training Data:
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- ImageNet
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Training Techniques:
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- AutoAugment
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- Cutout
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- Label Smoothing
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA 100 GPUs
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Architecture:
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- 1x1 Convolution
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- Anti-Alias Downsampling
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- Convolution
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- Global Average Pooling
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- InPlace-ABN
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- Leaky ReLU
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- ReLU
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- Residual Connection
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- Squeeze-and-Excitation Block
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File Size: 224440219
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Tasks:
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- Image Classification
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Training Time: ''
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ID: tresnet_l
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LR: 0.01
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L267
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Config: ''
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In Collection: TResNet
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- Name: tresnet_l_448
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Metadata:
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FLOPs: 43488238584
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Epochs: 300
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Training Data:
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- ImageNet
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Training Techniques:
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- AutoAugment
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- Cutout
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- Label Smoothing
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA 100 GPUs
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Architecture:
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- 1x1 Convolution
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- Anti-Alias Downsampling
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- Convolution
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- Global Average Pooling
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- InPlace-ABN
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- Leaky ReLU
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- ReLU
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- Residual Connection
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- Squeeze-and-Excitation Block
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File Size: 224440219
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Tasks:
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- Image Classification
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Training Time: ''
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ID: tresnet_l_448
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LR: 0.01
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '448'
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L285
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Config: ''
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In Collection: TResNet
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- Name: tresnet_m
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Metadata:
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FLOPs: 5733048064
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Epochs: 300
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Training Data:
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- ImageNet
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Training Techniques:
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- AutoAugment
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- Cutout
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- Label Smoothing
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA 100 GPUs
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Architecture:
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- 1x1 Convolution
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- Anti-Alias Downsampling
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- Convolution
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- Global Average Pooling
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- InPlace-ABN
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- Leaky ReLU
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- ReLU
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- Residual Connection
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- Squeeze-and-Excitation Block
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File Size: 125861314
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Tasks:
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- Image Classification
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Training Time: < 24 hours
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ID: tresnet_m
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LR: 0.01
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L261
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Config: ''
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In Collection: TResNet
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- Name: tresnet_m_448
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Metadata:
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FLOPs: 22929743104
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Epochs: 300
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Training Data:
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- ImageNet
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Training Techniques:
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- AutoAugment
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- Cutout
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- Label Smoothing
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA 100 GPUs
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Architecture:
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- 1x1 Convolution
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- Anti-Alias Downsampling
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- Convolution
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- Global Average Pooling
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- InPlace-ABN
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- Leaky ReLU
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- ReLU
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- Residual Connection
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- Squeeze-and-Excitation Block
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File Size: 125861314
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Tasks:
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- Image Classification
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Training Time: ''
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ID: tresnet_m_448
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LR: 0.01
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '448'
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L279
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Config: ''
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In Collection: TResNet
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- Name: tresnet_xl
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Metadata:
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FLOPs: 15162534034
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Epochs: 300
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Training Data:
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- ImageNet
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Training Techniques:
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- AutoAugment
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- Cutout
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- Label Smoothing
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA 100 GPUs
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Architecture:
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- 1x1 Convolution
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- Anti-Alias Downsampling
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- Convolution
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- Global Average Pooling
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- InPlace-ABN
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- Leaky ReLU
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- ReLU
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- Residual Connection
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- Squeeze-and-Excitation Block
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File Size: 314378965
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Tasks:
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- Image Classification
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Training Time: ''
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ID: tresnet_xl
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LR: 0.01
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L273
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Config: ''
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In Collection: TResNet
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- Name: tresnet_xl_448
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Metadata:
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FLOPs: 60641712730
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Epochs: 300
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Training Data:
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- ImageNet
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Training Techniques:
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- AutoAugment
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- Cutout
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- Label Smoothing
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- SGD with Momentum
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- Weight Decay
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Training Resources: 8x NVIDIA 100 GPUs
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Architecture:
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- 1x1 Convolution
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- Anti-Alias Downsampling
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- Convolution
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- Global Average Pooling
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- InPlace-ABN
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|
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- Leaky ReLU
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- ReLU
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- Residual Connection
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- Squeeze-and-Excitation Block
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File Size: 224440219
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Tasks:
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- Image Classification
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Training Time: ''
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ID: tresnet_xl_448
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LR: 0.01
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '448'
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L291
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Config: ''
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In Collection: TResNet
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Collections:
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- Name: TResNet
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Paper:
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title: 'TResNet: High Performance GPU-Dedicated Architecture'
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url: https://papperswithcode.com//paper/tresnet-high-performance-gpu-dedicated
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type: model-index
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Type: model-index
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-->
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