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134 lines
3.7 KiB
134 lines
3.7 KiB
# (Tensorflow) MixNet
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**MixNet** is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution).
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The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu).
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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{tan2019mixconv,
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title={MixConv: Mixed Depthwise Convolutional Kernels},
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author={Mingxing Tan and Quoc V. Le},
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year={2019},
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eprint={1907.09595},
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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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Type: model-index
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Collections:
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- Name: TF MixNet
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Paper:
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Title: 'MixConv: Mixed Depthwise Convolutional Kernels'
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URL: https://paperswithcode.com/paper/mixnet-mixed-depthwise-convolutional-kernels
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Models:
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- Name: tf_mixnet_l
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In Collection: TF MixNet
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Metadata:
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FLOPs: 688674516
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Parameters: 7330000
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File Size: 29620756
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Architecture:
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- Batch Normalization
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- Dense Connections
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- Dropout
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- Global Average Pooling
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- Grouped Convolution
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- MixConv
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- Squeeze-and-Excitation Block
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- Swish
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Tasks:
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- Image Classification
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Training Techniques:
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- MNAS
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Training Data:
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- ImageNet
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ID: tf_mixnet_l
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1720
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 78.78%
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Top 5 Accuracy: 94.0%
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- Name: tf_mixnet_m
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In Collection: TF MixNet
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Metadata:
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FLOPs: 416633502
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Parameters: 5010000
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File Size: 20310871
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Architecture:
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- Batch Normalization
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- Dense Connections
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- Dropout
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- Global Average Pooling
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- Grouped Convolution
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- MixConv
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- Squeeze-and-Excitation Block
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- Swish
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Tasks:
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- Image Classification
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Training Techniques:
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- MNAS
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Training Data:
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- ImageNet
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ID: tf_mixnet_m
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1709
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_m-0f4d8805.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 76.96%
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Top 5 Accuracy: 93.16%
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- Name: tf_mixnet_s
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In Collection: TF MixNet
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Metadata:
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FLOPs: 302587678
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Parameters: 4130000
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File Size: 16738218
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Architecture:
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- Batch Normalization
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- Dense Connections
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- Dropout
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- Global Average Pooling
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- Grouped Convolution
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- MixConv
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- Squeeze-and-Excitation Block
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- Swish
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Tasks:
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- Image Classification
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Training Techniques:
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- MNAS
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Training Data:
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- ImageNet
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ID: tf_mixnet_s
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1698
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_s-89d3354b.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 75.68%
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Top 5 Accuracy: 92.64%
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-->
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