利用pytorch对CIFAR数据进行图像分类（包含全套代码和10+个模型的实现）

# 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% |