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70 lines
2.1 KiB
70 lines
2.1 KiB
# FBNet
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**FBNet** is a type of convolutional neural architectures discovered through [DNAS](https://paperswithcode.com/method/dnas) neural architecture search. It utilises a basic type of image model block inspired by [MobileNetv2](https://paperswithcode.com/method/mobilenetv2) that utilises depthwise convolutions and an inverted residual structure (see components).
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The principal building block is the [FBNet Block](https://paperswithcode.com/method/fbnet-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{wu2019fbnet,
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title={FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search},
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author={Bichen Wu and Xiaoliang Dai and Peizhao Zhang and Yanghan Wang and Fei Sun and Yiming Wu and Yuandong Tian and Peter Vajda and Yangqing Jia and Kurt Keutzer},
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year={2019},
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eprint={1812.03443},
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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: fbnetc_100
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Metadata:
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FLOPs: 508940064
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Epochs: 360
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Batch Size: 256
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Training Data:
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- 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 GPUs
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Architecture:
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- 1x1 Convolution
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- Convolution
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- Dense Connections
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- Dropout
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- FBNet Block
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- Global Average Pooling
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- Softmax
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File Size: 22525094
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Tasks:
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- Image Classification
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ID: fbnetc_100
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LR: 0.1
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Layers: 22
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Dropout: 0.2
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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: 0.0005
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L985
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In Collection: FBNet
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Collections:
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- Name: FBNet
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Paper:
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title: 'FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural
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Architecture Search'
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url: https://paperswithcode.com//paper/fbnet-hardware-aware-efficient-convnet-design
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type: model-index
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Type: model-index
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-->
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