{"id":15936980,"url":"https://github.com/tyleryep/landmark","last_synced_at":"2025-09-02T13:38:18.337Z","repository":{"id":91173391,"uuid":"181732884","full_name":"TylerYep/landmark","owner":"TylerYep","description":"CS 230 Project","archived":false,"fork":false,"pushed_at":"2021-01-20T08:04:49.000Z","size":12758,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-09T08:16:58.720Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","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/TylerYep.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":"2019-04-16T17:07:04.000Z","updated_at":"2023-06-03T17:17:47.000Z","dependencies_parsed_at":"2023-05-30T20:30:24.029Z","dependency_job_id":null,"html_url":"https://github.com/TylerYep/landmark","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/TylerYep%2Flandmark","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TylerYep%2Flandmark/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TylerYep%2Flandmark/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TylerYep%2Flandmark/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/TylerYep","download_url":"https://codeload.github.com/TylerYep/landmark/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247070780,"owners_count":20878581,"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":[],"created_at":"2024-10-07T04:41:30.975Z","updated_at":"2025-04-03T19:44:10.237Z","avatar_url":"https://github.com/TylerYep.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Landmark Recognition\n#### CS 230 Project\n\nMain Challenge:\nhttps://www.kaggle.com/c/landmark-retrieval-2019/overview\n\nBaseline Model:\nhttps://www.kaggle.com/c/landmark-recognition-challenge/discussion/57919\n\n## Step 1: Install Conda Ennviroment\nRun ``` conda env create -f ennviroment.yml ```.\n\n### Step 2: Download Dataset CSV Link\nhttps://www.kaggle.com/c/landmark-retrieval-2019/data\nThe above link contains csv files with links to all of the images for the train and test sets. Unzip the folder and put it into data/images/, and then specify the number of examples you want to download in const.py. You can also manually change whether you want to download from the train, dev, or test set.\n\n### Step 3: Get Subset of Data\nRun ``` python preprocessing/subset-data.py ```.\n\n(Note: everything should be run from the ```landmark/``` level.)\n\nThis file outputs a modified ```train-subset.csv``` file to fetch images from. You can specify how many unique landmarks you want and how many of each you want by changing variables in ```const.py```. For our project, we will use 100,000 random images sampled from the full ```train.py``` dataset.\n\n### Step 3: Download Images\nRun ``` python download-images.py ```.\n\nHopefully this doesn't take forever. If you simply want all of the images, use the .sh file or download from a link on the Kaggle page.\n\n## Workflow\nBasically run train.py, which currently relies on three places: dataset, const, and layers.\n\ndataset.py\nconst.py\ntrain.py\ntest.py\nutil.py\n\n\n\n\n## To ask TAs:\n- Do we still want data augmentation when we have too much training data?\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftyleryep%2Flandmark","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftyleryep%2Flandmark","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftyleryep%2Flandmark/lists"}