{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/resnet50-on-cifar-100-without-transfer","title":"ResNet50_on_Cifar_100_Without_Transfer_Learning","arxiv_id":null,"date":"2020-08-03","proceeding":"Sorry, no such thing happened 2020 8","authors":["Batuhan Bayraktar"],"abstract":"I am a young person who is curious about image classification. \r\nI have an average knowledge and an average computer.\r\nI thought the cifar 100 data for image classification was challenging for me.\r\nIt was a data containing 100 classes from 32x32 and 100 images.\r\nI chose resnet as the model due to the low number of data and gradient vanishing problem.\r\nI worked with google colab because my computer is not enough (thank you Google)\r\nSince I used the free version, I could only run Resnet50.\r\nI tried to stick to the original Resnet article. But I made it myself in changes.\r\nI have tried many hyper parameters. \r\nI found the parameters that gave the best results as soon as possible as fast as I could.\r\nI'm a young man who likes to push his luck. that is all","url_abs":"https://github.com/batuhan3526/ResNet50_on_Cifar_100_Without_Transfer_Learning/blob/master/abstract.txt","url_pdf":"https://github.com/batuhan3526/ResNet50_on_Cifar_100_Without_Transfer_Learning/blob/master/abstract.txt","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"resnet50-on-cifar-100-without-transfer","repo_url":"https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssc_resnet50","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"resnet50-on-cifar-100-without-transfer","repo_url":"https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_resnet50","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"resnet50-on-cifar-100-without-transfer","repo_url":"https://github.com/2023-MindSpore-4/Code7/tree/main/ssc_resnet50","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"resnet50-on-cifar-100-without-transfer","repo_url":"https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet50","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"resnet50-on-cifar-100-without-transfer","repo_url":"https://github.com/Mind23-2/MindCode-69","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"resnet50-on-cifar-100-without-transfer","repo_url":"https://github.com/batuhan3526/ResNet50_on_Cifar_100_Without_Transfer_Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"am","method_name":"AM"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-cifar-100","task":"Image Classification","dataset":"CIFAR-100","model":"ResNet50 Without Transfer Learning","rank_in_archive_order":187,"of":211,"metrics":{"Percentage correct":"67.060"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}