Classification on CIFAR-10/100 and ImageNet with MindSpore.
- Install MindSpore
- Clone recursively
git clone https://github.com/Moranvl/MindSpore-classification.git - Download CIFAR for C programs and place them in "./data/cifar-10" and "./data/cifar-100"
- Download ImageNet 2012 and place them in "./data/ILSVRC2012/"
Please see the Training recipes for how to train the models.
Top1 error rate on the CIFAR-10/100 benchmarks are reported. You may get different results when training your models with different random seed. Note that the number of parameters are computed on the CIFAR-10 dataset.
| Model | Params (M) | CIFAR-10 (%) | CIFAR-100 (%) |
|---|---|---|---|
| alexnet | 2.47 | 36.71 | 64.50 |
| vgg19_bn | 20.04 | 12.12 | 40.67 |
| ResNet-110 | 1.73 | 15.73 | 49.41 |
| PreResNet-110 | 1.73 | 13.34 | 43.96 |
| ResNeXt-29, 8x64 | 34.43 | 8.96 | 34.13 |
| ResNeXt-29, 16x64 | 68.16 | 9.03 | 34.46 |
| DenseNet-BC (L=100, k=12) | 0.77 | 10.3 | 31.81 |
| DenseNet-BC (L=190, k=40) | 25.62 | 11.99 | 29.84 |
Single-crop (224x224) validation error rate is reported.
| Model | Params (M) | Top-1 Error (%) | Top-5 Error (%) |
|---|---|---|---|
| ResNet | 11.69 | 40.458 | 17.524 |
Since the size of images in CIFAR dataset is 32x32, popular network structures for ImageNet need some modifications to adapt this input size. The modified models is in the package models.cifar:
- AlexNet
- VGG (Imported from MindSpore-cifar)
- ResNet
- Pre-act-ResNet
- ResNeXt (Imported from ResNeXt.MindSpore)
- DenseNet
- All models in
mindcv.models(alexnet, vgg, resnet, densenet, inception_v3, squeezenet)
Feel free to create a pull request if you find any bugs or you want to contribute (e.g., more datasets and more network structures).