| Training Techniques | Polynomial Learning Rate Decay, Self-Adversarial Training, Weight Decay, SGD with Momentum, Label Smoothing, Mosaic, CutMix |
|---|---|
| Architecture | 1x1 Convolution, Batch Normalization, Convolution, Global Average Pooling, Mish, Residual Connection, Softmax |
| ID | cspdarknet53 |
| SHOW MORE |
CSPDarknet53 is a convolutional neural network and backbone for object detection that uses 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.
This CNN is used as the backbone for YOLOv4.
To load a pretrained model:
import timm
m = timm.create_model('cspdarknet53', pretrained=True)
m.eval()
Replace the model name with the variant you want to use, e.g. cspdarknet53. You can find the IDs in the model summaries at the top of this page.
You can follow the timm recipe scripts for training a new model afresh.
@misc{bochkovskiy2020yolov4,
title={YOLOv4: Optimal Speed and Accuracy of Object Detection},
author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao},
year={2020},
eprint={2004.10934},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Image Classification
| BENCHMARK | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK |
|---|---|---|---|---|
| ImageNet | cspdarknet53 | Top 1 Accuracy | 80.05% | # 98 |
| Top 5 Accuracy | 95.09% | # 98 |
