{"id":22615512,"url":"https://github.com/sovit-123/yolov1_pytorch","last_synced_at":"2025-03-29T00:42:25.271Z","repository":{"id":111788103,"uuid":"482391446","full_name":"sovit-123/yolov1_pytorch","owner":"sovit-123","description":"A simple implementation of YOLOv1 PyTorch and training on the PASCAL VOC dataset.","archived":false,"fork":false,"pushed_at":"2022-06-19T05:14:34.000Z","size":954,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-02-03T10:46:20.239Z","etag":null,"topics":["pascal-voc","yolo","yolov1","yolov1-pytorch"],"latest_commit_sha":null,"homepage":"","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/sovit-123.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":"2022-04-17T00:30:17.000Z","updated_at":"2022-05-22T08:28:32.000Z","dependencies_parsed_at":null,"dependency_job_id":"4387cd5a-586d-4c58-9e86-aa27d54ca2b0","html_url":"https://github.com/sovit-123/yolov1_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/sovit-123%2Fyolov1_pytorch","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sovit-123%2Fyolov1_pytorch/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sovit-123%2Fyolov1_pytorch/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sovit-123%2Fyolov1_pytorch/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sovit-123","download_url":"https://codeload.github.com/sovit-123/yolov1_pytorch/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246122243,"owners_count":20726822,"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":["pascal-voc","yolo","yolov1","yolov1-pytorch"],"created_at":"2024-12-08T19:08:13.666Z","updated_at":"2025-03-29T00:42:25.260Z","avatar_url":"https://github.com/sovit-123.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# YOLOV1 Training Pipeline on the PASCAL VOC Data using the PyTorch Framework\n\n\n\nThis repository contains YOLOV1 training pipeline on the PASCAL VOC 2007 and 2012 data using the PyTorch framework. **A few points:**\n\n\n\n## ***Note: Use this repository/code for any project/learning. A simple reference/attribution to the repository is enough. Completely OPEN for any use.*** \n\n\n\n## Steps to Train\n\n### First of All, Download and Extract the Data (Download to any directory of your choice)\n\n* [Download VOC 2007 trainval from here](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar).\n  * Or `wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar` in terminal.\n\n* [Download VOC 2007 test from here](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar).\n  * Or `wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar` in terminal.\n* [Download the VOC 2012 trainval from here](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar).\n  * Or `wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar`\n\n\n\nExecute the following commands in the directory where the downloaded `.tar` files are present.\n\n```\ntar xf VOCtrainval_06-Nov-2007.tar\n```\n\n```\ntar xf VOCtest_06-Nov-2007.tar \n```\n\n```\ntar xf VOCtrainval_11-May-2012.tar\n```\n\n\n\n### Prepare the Text Files and Labels\n\n* Then execute `prepare_data.py` with the correct path argument to the `VOCdevkit` parent directory. Simply, the next folder after the path in the arugment should be `VOCdevkit` The following is an example:\n\n  ```\n  python prepare_data.py --path my_pacal_voc_data\n  ```\n\n  In the above command `my_pacal_voc_data` should contain the `VOCdevkit` directory for the dataset.\n\n* Then execute `prepare_text_labels.py` with correct command line argument paths to the VOC 2007  and 2012  `Annotations` directory. See [`text_labels.py`](https://github.com/sovit-123/yolov1_pytorch_voc07/blob/main/prepare_text_labels.py) to know what the exact path should be. Example command:\n\n  ```\n  python prepare_text_labels.py --annotations-2007 VOCdevkit/VOC2007/Annotations/ --annotations-2012 VOCdevkit/VOC2012/Annotations/\n  ```\n\n  \n\n### Train\n\n* Execute `python train.py`. \n\n\n\n## References\n\n* [YOLOv1 paper](https://arxiv.org/pdf/1506.02640v5.pdf): https://arxiv.org/pdf/1506.02640v5.pdf\n* Some dataset preparation ideas borrowed from: https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/object_detection/YOLO/dataset.py\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsovit-123%2Fyolov1_pytorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsovit-123%2Fyolov1_pytorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsovit-123%2Fyolov1_pytorch/lists"}