{"id":16916613,"url":"https://github.com/ternaus/nexar2_ssd","last_synced_at":"2025-09-07T14:47:04.159Z","repository":{"id":87572959,"uuid":"110367581","full_name":"ternaus/nexar2_ssd","owner":"ternaus","description":null,"archived":false,"fork":false,"pushed_at":"2017-11-14T15:49:08.000Z","size":105729,"stargazers_count":2,"open_issues_count":4,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-04-05T13:33:57.905Z","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":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ternaus.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":"AUTHORS","dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-11-11T17:58:55.000Z","updated_at":"2017-11-11T21:10:12.000Z","dependencies_parsed_at":null,"dependency_job_id":"c716025e-9cd6-43d2-809e-8c61ee926462","html_url":"https://github.com/ternaus/nexar2_ssd","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ternaus/nexar2_ssd","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ternaus%2Fnexar2_ssd","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ternaus%2Fnexar2_ssd/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ternaus%2Fnexar2_ssd/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ternaus%2Fnexar2_ssd/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ternaus","download_url":"https://codeload.github.com/ternaus/nexar2_ssd/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ternaus%2Fnexar2_ssd/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":274051346,"owners_count":25214024,"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":"2025-09-07T02:00:09.463Z","response_time":67,"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":"2024-10-13T19:28:47.384Z","updated_at":"2025-09-07T14:47:04.135Z","avatar_url":"https://github.com/ternaus.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# SSD: Single Shot MultiBox Object Detector, in PyTorch for [NEXAR CHALLENGE II Vehicle Detection in the Wild using the NEXET Dataset](https://www.getnexar.com/challenge-2/)\nA [PyTorch](http://pytorch.org/) implementation of [Single Shot MultiBox Detector](http://arxiv.org/abs/1512.02325) from the 2016 paper by Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang, and Alexander C. Berg.  The official and original Caffe code can be found [here](https://github.com/weiliu89/caffe/tree/ssd).  \n\n\nCode is heavily based on (https://github.com/amdegroot/ssd.pytorch)\n\n\n\u003cimg align=\"right\" src= \"https://github.com/amdegroot/ssd.pytorch/blob/master/doc/ssd.png\" height = 400/\u003e\n\n### Table of Contents\n- \u003ca href='#installation'\u003eInstallation\u003c/a\u003e\n- \u003ca href='#training-ssd'\u003eTrain\u003c/a\u003e\n- \u003ca href='#evaluation'\u003eEvaluate\u003c/a\u003e\n- \u003ca href='#references'\u003eReference\u003c/a\u003e\n\n\u0026nbsp;\n\u0026nbsp;\n\u0026nbsp;\n\u0026nbsp;\n\n## Installation\n- Install [PyTorch](http://pytorch.org/) by selecting your environment on the website and running the appropriate command.\n- Clone this repository.\n  * Note: We currently only support Python 3+.\n- Then download the dataset by following the [instructions](#download-voc2007-trainval--test) below.\n- We now support [Visdom](https://github.com/facebookresearch/visdom) for real-time loss visualization during training! \n  * To use Visdom in the browser: \n  ```Shell\n  # First install Python server and client \n  pip install visdom\n  # Start the server (probably in a screen or tmux)\n  python -m visdom.server\n  ```\n  * Then (during training) navigate to http://localhost:8097/ (see the Train section below for training details).\n\n## Training SSD\n- First download the fc-reduced [VGG-16](https://arxiv.org/abs/1409.1556) PyTorch base network weights at:              https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth\n- By default, we assume you have downloaded the file in the `ssd.pytorch/weights` dir:\n\n```Shell\nmkdir weights\ncd weights\nwget https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth\n```\n\n- To train SSD using the train script simply specify the parameters listed in `train.py` as a flag or manually change them.\n\n```Shell\npython -m ssd_pytorch.train\n```\n\n- Note:\n  * For training, an NVIDIA GPU is strongly recommended for speed.\n  * Currently we only support training on v2 (the newest version).\n  * For instructions on Visdom usage/installation, see the \u003ca href='#installation'\u003eInstallation\u003c/a\u003e section.\n  * You can pick-up training from a checkpoint by specifying the path as one of the training parameters (again, see `train.py` for options)\n  \n## Evaluation\nTo evaluate a trained network:\n\n```Shell\npython -m ssd_pytorch.eval\n```\n\nYou can specify the parameters listed in the `eval.py` file by flagging them or manually changing them.  \n\n\n## References\n- [PyTorch implementation of SSD](https://github.com/amdegroot/ssd.pytorch)\n- Wei Liu, et al. \"SSD: Single Shot MultiBox Detector.\" [ECCV2016]((http://arxiv.org/abs/1512.02325)).\n- [Original Implementation (CAFFE)](https://github.com/weiliu89/caffe/tree/ssd)\n- A huge thank you to [Alex Koltun](https://github.com/alexkoltun) and his team at [Webyclip](webyclip.com) for their help in finishing the data augmentation portion.\n- A list of other great SSD ports that were sources of inspiration (especially the Chainer repo): \n  * [Chainer](https://github.com/Hakuyume/chainer-ssd), [Keras](https://github.com/rykov8/ssd_keras), [MXNet](https://github.com/zhreshold/mxnet-ssd), [Tensorflow](https://github.com/balancap/SSD-Tensorflow) \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fternaus%2Fnexar2_ssd","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fternaus%2Fnexar2_ssd","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fternaus%2Fnexar2_ssd/lists"}