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75 lines
2.2 KiB
75 lines
2.2 KiB
4 years ago
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# Summary
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**CSPDarknet53** is a convolutional neural network and backbone for object detection that uses [DarkNet-53](https://paperswithcode.com/method/darknet-53). It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network.
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This CNN is used as the backbone for [YOLOv4](https://paperswithcode.com/method/yolov4).
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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{bochkovskiy2020yolov4,
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title={YOLOv4: Optimal Speed and Accuracy of Object Detection},
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author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao},
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year={2020},
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eprint={2004.10934},
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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: cspdarknet53
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Metadata:
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FLOPs: 8545018880
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Batch Size: 128
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Training Data:
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- ImageNet
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Training Techniques:
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- CutMix
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- Label Smoothing
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- Mosaic
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- Polynomial Learning Rate Decay
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- SGD with Momentum
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- Self-Adversarial Training
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- Weight Decay
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Training Resources: 1x NVIDIA RTX 2070 GPU
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Global Average Pooling
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- Mish
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- Residual Connection
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- Softmax
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File Size: 110775135
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Tasks:
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- Image Classification
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ID: cspdarknet53
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LR: 0.1
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Layers: 53
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Crop Pct: '0.887'
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Momentum: 0.9
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Image Size: '256'
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Warmup Steps: 1000
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Weight Decay: 0.0005
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Interpolation: bilinear
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Training Steps: 8000000
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FPS (GPU RTX 2070): 66
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/cspnet.py#L441
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In Collection: CSP DarkNet
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Collections:
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- Name: CSP DarkNet
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Paper:
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title: 'YOLOv4: Optimal Speed and Accuracy of Object Detection'
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url: https://papperswithcode.com//paper/yolov4-optimal-speed-and-accuracy-of-object
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type: model-index
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Type: model-index
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-->
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