{"id":19070434,"url":"https://github.com/mindspore-courses/mindspore-classification","last_synced_at":"2025-10-13T22:16:22.219Z","repository":{"id":195666773,"uuid":"686887905","full_name":"mindspore-courses/mindspore-classification","owner":"mindspore-courses","description":"Image classification models with MindSpore.","archived":false,"fork":false,"pushed_at":"2023-09-19T00:59:51.000Z","size":128,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-02-22T03:43:37.575Z","etag":null,"topics":["cv","image-classification","mindspore","resnet"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mindspore-courses.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2023-09-04T06:52:31.000Z","updated_at":"2024-12-05T08:56:09.000Z","dependencies_parsed_at":"2023-09-19T05:54:33.276Z","dependency_job_id":null,"html_url":"https://github.com/mindspore-courses/mindspore-classification","commit_stats":null,"previous_names":["mindspore-courses/mindspore-classification"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mindspore-courses/mindspore-classification","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mindspore-courses%2Fmindspore-classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mindspore-courses%2Fmindspore-classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mindspore-courses%2Fmindspore-classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mindspore-courses%2Fmindspore-classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mindspore-courses","download_url":"https://codeload.github.com/mindspore-courses/mindspore-classification/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mindspore-courses%2Fmindspore-classification/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279017157,"owners_count":26085983,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-13T02:00:06.723Z","response_time":61,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cv","image-classification","mindspore","resnet"],"created_at":"2024-11-09T01:18:40.894Z","updated_at":"2025-10-13T22:16:22.196Z","avatar_url":"https://github.com/mindspore-courses.png","language":"Python","readme":"# MindSpore-classification\nClassification on CIFAR-10/100 and ImageNet with MindSpore.\n\n## Install\n* Install [MindSpore](https://www.mindspore.cn/)\n* Clone recursively\n  ```\n  git clone https://github.com/Moranvl/MindSpore-classification.git\n  ```\n* Download CIFAR for C programs and place them in \"./data/cifar-10\" and \"./data/cifar-100\"\n* Download [ImageNet 2012](https://image-net.org/challenges/LSVRC/index.php) and place them in \"./data/ILSVRC2012/\"\n\n## Training\nPlease see the [Training recipes](TRAINING.md) for how to train the models.\n\n## Results\n\n### CIFAR\nTop1 error rate on the CIFAR-10/100 benchmarks are reported. You may get different results when training your models with different random seed.\nNote that the number of parameters are computed on the CIFAR-10 dataset.\n\n| Model                     | Params (M) | CIFAR-10 (%) | CIFAR-100 (%) |\n| -------------------       |------------|--------------|---------------|\n| alexnet                   | 2.47       | 36.71        | 64.50         |\n| vgg19_bn                  | 20.04      | 12.12        | 40.67         |\n| ResNet-110                | 1.73       | 15.73        | 49.41         |\n| PreResNet-110             | 1.73       | 13.34        | 43.96         |\n| ResNeXt-29, 8x64          | 34.43      | 8.96         | 34.13         |\n| ResNeXt-29, 16x64         | 68.16      | 9.03         | 34.46         |\n| DenseNet-BC (L=100, k=12) | 0.77        | 10.3         | 31.81         |\n| DenseNet-BC (L=190, k=40) | 25.62      | 11.99        | 29.84         |\n\n\n### ImageNet\nSingle-crop (224x224) validation error rate is reported. \n\n\n| Model             | Params (M) | Top-1 Error (%) | Top-5 Error  (%) |\n| -------------------  |------------|-----------------|------------------|\n| ResNet            | 11.69      | 40.458          | 17.524           |\n\n[//]: # (| ResNeXt-50  |       |                |                |)\n\n\n## Supported Architectures\n\n### CIFAR-10 / CIFAR-100\nSince 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`:\n- [x] [AlexNet](https://arxiv.org/abs/1404.5997)\n- [x] [VGG](https://arxiv.org/abs/1409.1556) (Imported from [MindSpore-cifar](https://github.com/kuangliu/pytorch-cifar))\n- [x] [ResNet](https://arxiv.org/abs/1512.03385)\n- [x] [Pre-act-ResNet](https://arxiv.org/abs/1603.05027)\n- [x] [ResNeXt](https://arxiv.org/abs/1611.05431) (Imported from [ResNeXt.MindSpore](https://github.com/prlz77/ResNeXt.pytorch))\n- [x] [DenseNet](https://arxiv.org/abs/1608.06993)\n\n[//]: # (- [x] [Wide Residual Networks]\u0026#40;http://arxiv.org/abs/1605.07146\u0026#41; \u0026#40;Imported from [WideResNet-MindSpore]\u0026#40;https://github.com/xternalz/WideResNet-pytorch\u0026#41;\u0026#41;)\n\n\n### ImageNet\n- [x] All models in `mindcv.models` (alexnet, vgg, resnet, densenet, inception_v3, squeezenet)\n\n[//]: # (- [x] [ResNeXt]\u0026#40;https://arxiv.org/abs/1611.05431\u0026#41;)\n\n\n## Contribute\nFeel free to create a pull request if you find any bugs or you want to contribute (e.g., more datasets and more network structures).","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmindspore-courses%2Fmindspore-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmindspore-courses%2Fmindspore-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmindspore-courses%2Fmindspore-classification/lists"}