{"id":27992768,"url":"https://github.com/fastmachinelearning/cloud-segmentation","last_synced_at":"2025-05-08T18:42:19.936Z","repository":{"id":283267159,"uuid":"943794167","full_name":"fastmachinelearning/cloud-segmentation","owner":"fastmachinelearning","description":null,"archived":false,"fork":false,"pushed_at":"2025-04-15T12:48:42.000Z","size":548,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":15,"default_branch":"main","last_synced_at":"2025-04-15T13:45:41.866Z","etag":null,"topics":[],"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/fastmachinelearning.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":"2025-03-06T09:23:33.000Z","updated_at":"2025-04-15T12:48:45.000Z","dependencies_parsed_at":"2025-03-19T11:27:37.018Z","dependency_job_id":"db9332d5-2ecf-4b1b-80ef-d93c3f117667","html_url":"https://github.com/fastmachinelearning/cloud-segmentation","commit_stats":null,"previous_names":["fastmachinelearning/cloud-segmentation"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fastmachinelearning%2Fcloud-segmentation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fastmachinelearning%2Fcloud-segmentation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fastmachinelearning%2Fcloud-segmentation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fastmachinelearning%2Fcloud-segmentation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/fastmachinelearning","download_url":"https://codeload.github.com/fastmachinelearning/cloud-segmentation/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253131275,"owners_count":21858960,"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":"2025-05-08T18:42:16.344Z","updated_at":"2025-05-08T18:42:19.921Z","avatar_url":"https://github.com/fastmachinelearning.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Package description\nThis folder contains example training scripts of AGENIUM SPACE tiny unet model on ALCD Cloud DB. It contains training script, the ALCD DB reprocessed in RGB a docker recipe.\n\n# Docker\nThe folder contains a docker recipe and a make file to build an run it, got check the Readme in DOCKER folder. It is assumed you have a NVIDIA GPU on your computer.\nTo build the docker:\n```sh\ncd DOCKER\nmake build\n```\n\nTo run the docker : \n```sh\ncd DOCKER\nmake bash\n```\n\n# Database \nYou can find the DB in DATA. It is a tiled and rgb format of the ALCD Cloud DB available online [here](https://zenodo.org/records/1460961).\n\n# Trained Models\nThis section assume you are using the provided docker.\n\nFor training the tiny unet 100k model use :\n```sh\n    python ./SCRIPTS/train.py run --model ags_tiny_unet_100k --data_path ./DATA/\n```\n\nFor training the tiny unet 50k model use :\n```sh\n    python ./SCRIPTS/train.py run --model ags_tiny_unet_50k --data_path ./DATA/\n```\n\nYou can access the help info using\n```sh\n    python ./SCRIPTS/train.py run -- --help\n```\n\nTo use 2 gpus:\n```bash\n# using torchrun\ntorchrun --nproc_per_node=2 ./SCRIPTS/train.py run --model ags_tiny_unet_100k --data_path ./DATA/ --backend=\"nccl\"\n```\n\npython ./SCRIPTS/train.py run --model ags_tiny_unet_50k --data_path ./DATA/\n\n\n# Models Score\nThe table below contains the score results for the the models\n\n| Model | Train Score (mean F1-score) | Valid Score (F1-score) |\n|-------|-------------------|----------------|\n| tiny_unet_50k | 0.94 - 0.78 | 0.94 - 0.77 |\n| tiny_unet_100k | 0.94 - 0.79 | 0.84 - 0.78 |\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffastmachinelearning%2Fcloud-segmentation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffastmachinelearning%2Fcloud-segmentation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffastmachinelearning%2Fcloud-segmentation/lists"}