{"id":13627459,"url":"https://github.com/zhreshold/mxnet-yolo","last_synced_at":"2025-04-13T06:37:13.471Z","repository":{"id":67409550,"uuid":"89751121","full_name":"zhreshold/mxnet-yolo","owner":"zhreshold","description":"YOLO: You only look once real-time object detector","archived":false,"fork":false,"pushed_at":"2018-10-22T17:52:37.000Z","size":1808,"stargazers_count":237,"open_issues_count":28,"forks_count":85,"subscribers_count":25,"default_branch":"master","last_synced_at":"2025-03-26T23:06:23.327Z","etag":null,"topics":["object-detection","yolo","yolo2","you-only-look-once"],"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/zhreshold.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}},"created_at":"2017-04-28T23:10:45.000Z","updated_at":"2025-01-01T04:16:13.000Z","dependencies_parsed_at":"2023-02-24T20:15:08.746Z","dependency_job_id":null,"html_url":"https://github.com/zhreshold/mxnet-yolo","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zhreshold%2Fmxnet-yolo","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zhreshold%2Fmxnet-yolo/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zhreshold%2Fmxnet-yolo/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zhreshold%2Fmxnet-yolo/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/zhreshold","download_url":"https://codeload.github.com/zhreshold/mxnet-yolo/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248675337,"owners_count":21143763,"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":["object-detection","yolo","yolo2","you-only-look-once"],"created_at":"2024-08-01T22:00:34.310Z","updated_at":"2025-04-13T06:37:13.446Z","avatar_url":"https://github.com/zhreshold.png","language":"Python","funding_links":[],"categories":["\u003ca name=\"Vision\"\u003e\u003c/a\u003e2. Vision","Python","Other Versions of YOLO"],"sub_categories":["2.2 Object Detection"],"readme":"# YOLO-v2: Real-Time Object Detection\n\nStill under development. 71 mAP(darknet) and 74mAP(resnet50) on VOC2007 achieved so far.\n\nThis is a pre-released version.\n\n### What's new\nThis repo is now deprecated, I am migrating to the latest [Gluon-CV](https://github.com/dmlc/gluon-cv) which is more user friendly and has a lot more algorithms in development. \n\n* Pretrained YOLOv3 models which achiveve 81%+ mAP on VOC and near 37% mAP on COCO: [Model Zoo](https://gluon-cv.mxnet.io/model_zoo/detection.html).\n\n* Object Detection model [tutorials](https://gluon-cv.mxnet.io/build/examples_detection/index.html).\n\n\nThis repo will not receive active development, however, you can continue use it with the mxnet 1.1.0(probably 1.2.0).\n\n### Disclaimer\nThis is a re-implementation of original yolo v2 which is based on [darknet](https://github.com/pjreddie/darknet).\nThe arXiv paper is available [here](https://arxiv.org/pdf/1612.08242.pdf).\n\n### Demo\n\n![demo1](https://user-images.githubusercontent.com/3307514/28980832-29bb0262-7904-11e7-83e3-a5fec65e0c70.png)\n\n### Getting started\n- Build from source, this is required because this example is not merged, some\ncustom operators are not presented in official MXNet. [Instructions](http://mxnet.io/get_started/install.html)\n- Install required packages: `cv2`, `matplotlib`\n\n### Try the demo\n- Download the pretrained [model](https://github.com/zhreshold/mxnet-yolo/releases/download/0.1-alpha/yolo2_darknet19_416_pascalvoc0712_trainval.zip)(darknet as backbone), or this [model](https://github.com/zhreshold/mxnet-yolo/releases/download/v0.2.0/yolo2_resnet50_voc0712_trainval.tar.gz)(resnet50 as backbone) and extract to `model/` directory.\n- Run\n```\n# cd /path/to/mxnet-yolo\npython demo.py --cpu\n# available options\npython demo.py -h\n```\n\n### Train the model\n- Grab a pretrained model, e.g. [`darknet19`](https://github.com/zhreshold/mxnet-yolo/releases/download/0.1-alpha/darknet19_416_ILSVRC2012.zip)\n- (optional) Grab a pretrained resnet50 model, [`resnet-50-0000.params`](http://data.dmlc.ml/models/imagenet/resnet/50-layers/resnet-50-0000.params),[`resnet-50-symbol.json`](http://data.dmlc.ml/models/imagenet/resnet/50-layers/resnet-50-symbol.json), this will produce slightly better mAP than `darknet` in my experiments.\n- Download PASCAL VOC dataset.\n```\ncd /path/to/where_you_store_datasets/\nwget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar\nwget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar\nwget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar\n# Extract the data.\ntar -xvf VOCtrainval_11-May-2012.tar\ntar -xvf VOCtrainval_06-Nov-2007.tar\ntar -xvf VOCtest_06-Nov-2007.tar\nln -s /path/to/VOCdevkit /path/to/mxnet-yolo/data/VOCdevkit\n```\n- Create packed binary file for faster training\n```\n# cd /path/to/mxnet-ssd\nbash tools/prepare_pascal.sh\n# or if you are using windows\npython tools/prepare_dataset.py --dataset pascal --year 2007,2012 --set trainval --target ./data/train.lst\npython tools/prepare_dataset.py --dataset pascal --year 2007 --set test --target ./data/val.lst --shuffle False\n```\n- Start training\n```\npython train.py --gpus 0,1,2,3 --epoch 0\n# choose different networks, such as resnet50_yolo\npython train.py --gpus 0,1,2,3 --network resnet50_yolo --data-shape 416 --pretrained model/resnet-50 --epoch 0\n# see advanced arguments for training\npython train.py -h\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzhreshold%2Fmxnet-yolo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzhreshold%2Fmxnet-yolo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzhreshold%2Fmxnet-yolo/lists"}