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78 lines
2.2 KiB
78 lines
2.2 KiB
# ESE VoVNet
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**VoVNet** is a convolutional neural network that seeks to make [DenseNet](https://paperswithcode.com/method/densenet) more efficient by concatenating all features only once in the last feature map, which makes input size constant and enables enlarging new output channel.
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Read about [one-shot aggregation here](https://paperswithcode.com/method/one-shot-aggregation).
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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{lee2019energy,
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title={An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection},
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author={Youngwan Lee and Joong-won Hwang and Sangrok Lee and Yuseok Bae and Jongyoul Park},
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year={2019},
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eprint={1904.09730},
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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: ese_vovnet39b
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Metadata:
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FLOPs: 9089259008
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Training Data:
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- ImageNet
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Architecture:
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- Batch Normalization
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- Convolution
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- Max Pooling
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- One-Shot Aggregation
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- ReLU
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File Size: 98397138
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Tasks:
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- Image Classification
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ID: ese_vovnet39b
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Layers: 39
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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/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/vovnet.py#L371
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In Collection: ESE VovNet
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- Name: ese_vovnet19b_dw
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Metadata:
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FLOPs: 1711959904
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Training Data:
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- ImageNet
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Architecture:
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- Batch Normalization
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- Convolution
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- Max Pooling
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- One-Shot Aggregation
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- ReLU
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File Size: 26243175
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Tasks:
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- Image Classification
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ID: ese_vovnet19b_dw
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Layers: 19
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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/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/vovnet.py#L361
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In Collection: ESE VovNet
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Collections:
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- Name: ESE VovNet
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Paper:
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title: 'CenterMask : Real-Time Anchor-Free Instance Segmentation'
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url: https://paperswithcode.com//paper/centermask-real-time-anchor-free-instance-1
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type: model-index
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Type: model-index
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-->
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