{"id":22120168,"url":"https://github.com/joshbrew/xgboost_onnx_training_conversion","last_synced_at":"2026-07-02T08:32:55.393Z","repository":{"id":220202962,"uuid":"751028535","full_name":"joshbrew/XGBoost_ONNX_Training_Conversion","owner":"joshbrew","description":"Image and CSV classification samples using ONNX with (bad) sample data.","archived":false,"fork":false,"pushed_at":"2024-03-22T00:25:35.000Z","size":57453,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-30T18:48:26.768Z","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":"lgpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/joshbrew.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":"2024-01-31T19:44:56.000Z","updated_at":"2024-02-01T08:38:57.000Z","dependencies_parsed_at":"2024-03-22T01:30:09.770Z","dependency_job_id":"8e97ecee-07ab-413b-8ef7-88bf997fe341","html_url":"https://github.com/joshbrew/XGBoost_ONNX_Training_Conversion","commit_stats":null,"previous_names":["joshbrew/xgboost_onnx_training_conversion"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/joshbrew/XGBoost_ONNX_Training_Conversion","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/joshbrew%2FXGBoost_ONNX_Training_Conversion","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/joshbrew%2FXGBoost_ONNX_Training_Conversion/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/joshbrew%2FXGBoost_ONNX_Training_Conversion/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/joshbrew%2FXGBoost_ONNX_Training_Conversion/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/joshbrew","download_url":"https://codeload.github.com/joshbrew/XGBoost_ONNX_Training_Conversion/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/joshbrew%2FXGBoost_ONNX_Training_Conversion/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35040021,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-07-02T02:00:06.368Z","response_time":173,"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":[],"created_at":"2024-12-01T14:21:17.183Z","updated_at":"2026-07-02T08:32:55.375Z","avatar_url":"https://github.com/joshbrew.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# XGBoost_ONNX_Notebooks\nImage and CSV classification samples using ONNX with sample data. This demonstrates the powerful, minimal [XGBoost](https://xgboost.readthedocs.io/en/stable/) algorithm meant for portability while retaining competitive classification accuracy.\n\nThese are beginner-friendly python notebooks with plenty of instructions for creating ONNX models that can be loaded into your own software applications to get the power of offline machine learning into your phone or browser. You can see our results in action, running in milliseconds or even nanoseconds on WebGPU or WebGL at: [https://github.com/joshbrew/cameraId-wonnx-wasm](https://github.com/joshbrew/cameraId-wonnx-wasm)\n\nFollow the instructions in the XGBoost subfolders for training the respective models on your local machine. \n\nThey are fairly identical processes. Also find google colab links in those READMEs for trying it out online.\n\nWe're working on getting better datasets, they are homemade for fish identification from digital spectral data and images of filets. This is part of a project for creating image recognition tools for conservation purposes with funding from Schmidt Marine and the Oregon Coast Aquarium.\n\nMore info forthcoming.\n\n\n\n## Credits\n\n- .ipynb notebooks by Mateusz Stankiewicz  ([flyps.io](https://flyps.io)) and Joshua Brewster\n- datasets by Joshua Brewster (WIP)\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjoshbrew%2Fxgboost_onnx_training_conversion","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjoshbrew%2Fxgboost_onnx_training_conversion","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjoshbrew%2Fxgboost_onnx_training_conversion/lists"}