{"id":22418933,"url":"https://github.com/en10/cifar","last_synced_at":"2025-08-20T07:30:52.084Z","repository":{"id":75872095,"uuid":"106687879","full_name":"EN10/CIFAR","owner":"EN10","description":"CIFAR 10 image dataset","archived":false,"fork":false,"pushed_at":"2024-11-20T20:25:23.000Z","size":29,"stargazers_count":48,"open_issues_count":0,"forks_count":12,"subscribers_count":4,"default_branch":"master","last_synced_at":"2024-12-05T16:13:21.997Z","etag":null,"topics":["cifar","cifar-10","cifar10","gradient-descent","logistic-regression","sigmoid"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/EN10.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-10-12T12:09:49.000Z","updated_at":"2024-11-20T20:25:27.000Z","dependencies_parsed_at":"2023-02-24T10:15:48.466Z","dependency_job_id":null,"html_url":"https://github.com/EN10/CIFAR","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EN10%2FCIFAR","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EN10%2FCIFAR/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EN10%2FCIFAR/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EN10%2FCIFAR/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/EN10","download_url":"https://codeload.github.com/EN10/CIFAR/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":230400616,"owners_count":18219831,"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","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":["cifar","cifar-10","cifar10","gradient-descent","logistic-regression","sigmoid"],"created_at":"2024-12-05T16:13:23.955Z","updated_at":"2024-12-19T08:09:16.729Z","avatar_url":"https://github.com/EN10.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# CIFAR 10 in Python\n\nThe CIFAR-10 dataset consists of 60000 32x32 colour images in [10 classes](https://github.com/EN10/CIFAR#classes), with 6000 images per class.  \nThere are 50000 training images and 10000 test images.  \nThe dataset is divided into five training batches and one test batch, each with 10000 images.\n\n[CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html)  \n\n### Download\n\n    wget -c https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n\n* [CIFAR 10 Python](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)\n\n### Extract\n\n    tar -xvzf cifar-10-python.tar.gz\n\n### Files:\n\n* `sigmoid.py` : [Based on DL.ai cats.py](https://github.com/EN10/DL.ai/blob/master/w2/cats.py)\n\n* `5_layer.py:` : [Based on DL.ai 5_layer_model.py](https://github.com/EN10/DL.ai/blob/master/w4/5_layer_model.py)\n\n* `load_cifar.py` : load `data_batch_1` data (images) and labels (classes) into a np.array \n\n* `imsave.py` : save an image from CIFAR-10 as JPG\n\n* `class_labels.py` : load `batches.meta` label_names (classes) and print them  \n\n* `filter_class.py` : write class label index from `data_batch_1` to class_label.txt\n\n### Accuracy\n`load_cifar.py` uses `random.seed(1)` for consistant accuracy  \n\n`sigmoid.py:`\n\n    train accuracy: 89.0 %\n    test accuracy: 72.0 %\n\n`5_layer.py:`\n\n    train accuracy: 99.0 %\n    test accuracy: 70.0 %\n\n### Classes:\n\n0 : airplane  \n1 : automobile  \n2 : bird  \n3 : cat  \n4 : deer  \n5 : dog  \n6 : frog  \n7 : horse  \n8 : ship  \n9 : truck  \n\n### Ref:\n\n[Batch to Array Code](https://gist.github.com/juliensimon/273bef4c5b4490c687b2f92ee721b546)\n\n### Compatability\n[imsave.py](https://github.com/EN10/CIFAR/blob/master/imsave.py) uses `from scipy.misc import imsave` which is deprecated  \nthis requires `sudo pip install scipy==0.16.1`  \n[save.py](https://github.com/EN10/KerasCIFAR/blob/master/save.py) PIL version","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fen10%2Fcifar","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fen10%2Fcifar","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fen10%2Fcifar/lists"}