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https://github.com/mindspore-courses/mindspore-classification
Image classification models with MindSpore.
https://github.com/mindspore-courses/mindspore-classification
cv image-classification mindspore resnet
Last synced: about 1 month ago
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Image classification models with MindSpore.
- Host: GitHub
- URL: https://github.com/mindspore-courses/mindspore-classification
- Owner: mindspore-courses
- License: mit
- Created: 2023-09-04T06:52:31.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2023-09-19T00:59:51.000Z (over 1 year ago)
- Last Synced: 2024-04-21T11:27:01.129Z (8 months ago)
- Topics: cv, image-classification, mindspore, resnet
- Language: Python
- Homepage:
- Size: 125 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# MindSpore-classification
Classification on CIFAR-10/100 and ImageNet with MindSpore.
## Install
* Install [MindSpore](https://www.mindspore.cn/)
* 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](https://image-net.org/challenges/LSVRC/index.php) and place them in "./data/ILSVRC2012/"
## Training
Please see the [Training recipes](TRAINING.md) for how to train the models.
## Results
### CIFAR
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 |
### ImageNet
Single-crop (224x224) validation error rate is reported.
| Model | Params (M) | Top-1 Error (%) | Top-5 Error (%) |
| ------------------- |------------|-----------------|------------------|
| ResNet | 11.69 | 40.458 | 17.524 |
[//]: # (| ResNeXt-50 | | | |)
## Supported Architectures
### CIFAR-10 / CIFAR-100
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`:
- [x] [AlexNet](https://arxiv.org/abs/1404.5997)
- [x] [VGG](https://arxiv.org/abs/1409.1556) (Imported from [MindSpore-cifar](https://github.com/kuangliu/pytorch-cifar))
- [x] [ResNet](https://arxiv.org/abs/1512.03385)
- [x] [Pre-act-ResNet](https://arxiv.org/abs/1603.05027)
- [x] [ResNeXt](https://arxiv.org/abs/1611.05431) (Imported from [ResNeXt.MindSpore](https://github.com/prlz77/ResNeXt.pytorch))
- [x] [DenseNet](https://arxiv.org/abs/1608.06993)
[//]: # (- [x] [Wide Residual Networks](http://arxiv.org/abs/1605.07146) (Imported from [WideResNet-MindSpore](https://github.com/xternalz/WideResNet-pytorch)))
### ImageNet
- [x] All models in `mindcv.models` (alexnet, vgg, resnet, densenet, inception_v3, squeezenet)
[//]: # (- [x] [ResNeXt](https://arxiv.org/abs/1611.05431))
## Contribute
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).