{"id":15638951,"url":"https://github.com/dansuh17/alexnet-pytorch","last_synced_at":"2025-04-10T01:15:36.789Z","repository":{"id":40986223,"uuid":"141377518","full_name":"dansuh17/alexnet-pytorch","owner":"dansuh17","description":"Pytorch Implementation of AlexNet","archived":false,"fork":false,"pushed_at":"2023-10-03T23:52:17.000Z","size":2110,"stargazers_count":200,"open_issues_count":3,"forks_count":59,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-10T01:15:30.464Z","etag":null,"topics":["alexnet","dataset","paper","pytorch"],"latest_commit_sha":null,"homepage":null,"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/dansuh17.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":"2018-07-18T03:43:22.000Z","updated_at":"2025-04-03T06:59:17.000Z","dependencies_parsed_at":"2024-10-23T04:37:14.374Z","dependency_job_id":null,"html_url":"https://github.com/dansuh17/alexnet-pytorch","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/dansuh17%2Falexnet-pytorch","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dansuh17%2Falexnet-pytorch/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dansuh17%2Falexnet-pytorch/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dansuh17%2Falexnet-pytorch/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dansuh17","download_url":"https://codeload.github.com/dansuh17/alexnet-pytorch/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248137891,"owners_count":21053775,"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":["alexnet","dataset","paper","pytorch"],"created_at":"2024-10-03T11:24:07.147Z","updated_at":"2025-04-10T01:15:36.765Z","avatar_url":"https://github.com/dansuh17.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Pytorch implementation of AlexNet\n\n- Now compatible with `pytorch==0.4.0`\n\nThis is an implementaiton of AlexNet, as introduced in the paper \"ImageNet Classification with Deep Convolutional Neural Networks\" by Alex Krizhevsky et al. ([original paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf))\n\nThis was the first very successful CNN for image classification that led to breakout of deep learning 'hype', as well as the first successful example of utilizing dropout layers.\n\n## Prerequisites\n\n- python \u003e= 3.5\n- pytorch==0.4.0\n\nYou can install required packages by:\n\n```bash\npip3 install -r requirements.txt\n```\n\n## DataSet\n\nThis implemenation uses the [ILSVRC 2012 dataset](http://www.image-net.org/challenges/LSVRC/2012/), also known as the 'ImageNet 2012 dataset'.\nThe data size is dreadfully large (138G!), but this amount of large-sized dataset is required for successful training of AlexNet.\nTesting with [Tiny ImageNet](https://tiny-imagenet.herokuapp.com/) or [MNIST](http://yann.lecun.com/exdb/mnist/) could not be done due to their smaller feature sizes (images do not fit the input size 227 x 227).\n\nAfter downloading the dataset file (i.e., `ILSVRC2012_img_train.tar`), use `extract_imagenet.sh` to extract the entire dataset. \n\n```bash\nextract_imagenet.sh\n```\n\nImageNet 2012's dataset structure is already arranged as `/root/[class]/[img_id].jpeg`, so using `torchvision.datasets.ImageFolder` is convenient.\n\n\n## Training\n\n```bash\npython3 model.py\n```\n\nSpecify the data path by modifying the constant `TRAIN_IMG_DIR` at the beginning of the script.\nAlso tune model parameters by modifying constants at the beginning of the script.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdansuh17%2Falexnet-pytorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdansuh17%2Falexnet-pytorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdansuh17%2Falexnet-pytorch/lists"}