{"id":21441449,"url":"https://github.com/tum-vision/lgm","last_synced_at":"2025-10-14T22:07:22.732Z","repository":{"id":150267462,"uuid":"168581644","full_name":"tum-vision/lgm","owner":"tum-vision","description":"Implementation of Layered Graphical Model with demo code ","archived":false,"fork":false,"pushed_at":"2019-04-05T11:47:14.000Z","size":55,"stargazers_count":4,"open_issues_count":0,"forks_count":3,"subscribers_count":12,"default_branch":"master","last_synced_at":"2025-10-14T22:07:21.534Z","etag":null,"topics":["cpp","graphical-models","machine-learning","python","pytorch"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tum-vision.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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-01-31T19:15:45.000Z","updated_at":"2024-01-04T16:30:21.000Z","dependencies_parsed_at":"2023-04-12T20:30:43.531Z","dependency_job_id":null,"html_url":"https://github.com/tum-vision/lgm","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/tum-vision/lgm","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tum-vision%2Flgm","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tum-vision%2Flgm/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tum-vision%2Flgm/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tum-vision%2Flgm/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tum-vision","download_url":"https://codeload.github.com/tum-vision/lgm/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tum-vision%2Flgm/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279021744,"owners_count":26087053,"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":"2025-10-14T02:00:06.444Z","response_time":60,"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":["cpp","graphical-models","machine-learning","python","pytorch"],"created_at":"2024-11-23T01:26:23.367Z","updated_at":"2025-10-14T22:07:22.718Z","avatar_url":"https://github.com/tum-vision.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# the LGM package\n\nby _Yuesong Shen_\n\nThis repository contains the demo code (as a python package) for the paper:\n\n\"Probabilistic Discriminative Learning with Layered Graphical Models\" by\nYuesong Shen, Tao Wu, Csaba Domokos and Daniel Cremers\n\nThe code is released under GPL v3 or later. For any questions please contact:\nyuesong.shen@tum.de\n\n## setup instructions:\n\nTested environment: Ubuntu 16.04; Python 3.6; gcc 5.4.0.\n\nRequired dependencies: Python 3.5+ along with pip; ABI compatible C++ compiler.\n\n- In terminal, change to current directory.\n\n- Install dependencies: \"pip install -r requirements.txt\"\n\n- Install locally the demo package: \"pip install -e .\"\n\n\n## usage instructions:\n\nDemo scripts are inside the folder \"example/\".\n\n- \"demo_lgm.py\" is the demo script for LGM models\n\n  Run \"python demo_lgm.py -h\" for possible arguments\n\n  Examples:\n\n  - Run Conv model with TRW and FashionMNIST. Use cuda:\n\n    \"python demo_lgm.py -m conv -i trw -d FashionMNIST -g\"\n\n  - run Dense model with LBP (2 inference iterations) and MNIST for 10 epochs.\n    Use cpu only:\n\n    \"python demo_lgm.py -m dense -i loopy -n 2 -d MNIST -e 10\"\n\n- \"demo_nn.py\" is the demo script for NN baselines\n\n  Run \"python demo_nn.py -h\" for possible arguments\n\n  Examples:\n\n  - Run Conv model with FashionMNIST and sigmoid activation. Use cuda:\n\n    \"python demo_nn.py -m conv -a sigmoid -d FashionMNIST -g\"\n\n  - run Dense model with relu and MNIST for 10 epochs. Use cpu only:\n\n    \"python demo_nn.py -m dense -a relu -d MNIST -e 10\"\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftum-vision%2Flgm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftum-vision%2Flgm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftum-vision%2Flgm/lists"}