# Train CIFAR10 with PyTorch
I'm playing with [PyTorch](http://pytorch.org/) on the CIFAR10 dataset.
## Prerequisites
- Python 3.6+
- PyTorch 1.0+
## Training
```
# Start training with:
python main.py
# You can manually resume the training with:
python main.py --resume --lr=0.01
```
## Accuracy
| Model | Acc. |
| ----------------- | ----------- |
| [VGG16](https://arxiv.org/abs/1409.1556) | 92.64% |
| [ResNet18](https://arxiv.org/abs/1512.03385) | 93.02% |
| [ResNet50](https://arxiv.org/abs/1512.03385) | 93.62% |
| [ResNet101](https://arxiv.org/abs/1512.03385) | 93.75% |
| [RegNetX_200MF](https://arxiv.org/abs/2003.13678) | 94.24% |
| [RegNetY_400MF](https://arxiv.org/abs/2003.13678) | 94.29% |
| [MobileNetV2](https://arxiv.org/abs/1801.04381) | 94.43% |
| [ResNeXt29(32x4d)](https://arxiv.org/abs/1611.05431) | 94.73% |
| [ResNeXt29(2x64d)](https://arxiv.org/abs/1611.05431) | 94.82% |
| [SimpleDLA](https://arxiv.org/abs/1707.064) | 94.89% |
| [DenseNet121](https://arxiv.org/abs/1608.06993) | 95.04% |
| [PreActResNet18](https://arxiv.org/abs/1603.05027) | 95.11% |
| [DPN92](https://arxiv.org/abs/1707.01629) | 95.16% |
| [DLA](https://arxiv.org/pdf/1707.06484.pdf) | 95.47% |
基于Pytorch训练CIRAR10上分类算法.zip
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2024-05-22
06:27:29
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基于Pytorch训练CIRAR10上分类算法.zip （22个子文件） 
基于Pytorch训练CIRAR10上分类算法 
utils.py 3KB main.py 5KB models __init__.py 427B lenet.py 699B efficientnet.py 6KB regnet.py 4KB googlenet.py 3KB shufflenet.py 3KB dpn.py 3KB resnext.py 3KB shufflenetv2.py 5KB mobilenetv2.py 3KB dla.py 4KB senet.py 4KB densenet.py 3KB resnet.py 4KB vgg.py 1KB dla_simple.py 4KB pnasnet.py 4KB mobilenet.py 2KB preact_resnet.py 4KB README.md 1KB资源评论
