{"id":19066714,"url":"https://github.com/skp-1997/deepdishtracker","last_synced_at":"2026-05-15T18:30:17.273Z","repository":{"id":199621977,"uuid":"703337866","full_name":"skp-1997/DeepDishTracker","owner":"skp-1997","description":"The objective is to count the plates using object detection and object tracking. ","archived":false,"fork":false,"pushed_at":"2024-02-01T22:13:30.000Z","size":20,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-02T14:34:46.934Z","etag":null,"topics":["deep-learning","detectron2","fater-rcnn","object-counting","object-detection","python","tracking-algorithm"],"latest_commit_sha":null,"homepage":"","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/skp-1997.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}},"created_at":"2023-10-11T04:08:23.000Z","updated_at":"2024-08-17T08:42:57.000Z","dependencies_parsed_at":null,"dependency_job_id":"c7a36b51-7bfa-4112-b3d5-206f3cbf50cc","html_url":"https://github.com/skp-1997/DeepDishTracker","commit_stats":null,"previous_names":["skp-1997/count_object_dishes","skp-1997/deepdishtracker"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/skp-1997%2FDeepDishTracker","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/skp-1997%2FDeepDishTracker/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/skp-1997%2FDeepDishTracker/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/skp-1997%2FDeepDishTracker/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/skp-1997","download_url":"https://codeload.github.com/skp-1997/DeepDishTracker/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240118418,"owners_count":19750491,"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":["deep-learning","detectron2","fater-rcnn","object-counting","object-detection","python","tracking-algorithm"],"created_at":"2024-11-09T00:57:47.693Z","updated_at":"2026-05-15T18:30:17.233Z","avatar_url":"https://github.com/skp-1997.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Counting the dishes using detection and tracking algorithm\nThe objective is to count the plates using object detection and object tracking. The repository used the PyimageSearch Simple Object Tracking code frome [here](https://pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/). The object detection model used is pre-trained Faster R-CNN model trained on [Detectron2](https://github.com/facebookresearch/detectron2) framework and a custom dataset.\n\n\n\n# Output of the code counting the dishes!\n\n![ezgif com-crop (1)](https://github.com/skp-1997/Count_Object_Dishes/assets/97504177/fb932ec8-bb5a-4111-bd09-48dd731c3d2c)\n\n\n# Installation of the environment\n\nWe encourage you to use conda environment. Once you create an environment use follwoing command to get the environment ready\n\n```\npip install -r requirements.txt\n```\n\n# Training the Faster R-CNN Model\n\nFollow instruction given in PlateCount_FasterRCNN.ipynb file for training the model. You can use the data from the roboflow account provided in the file.\n\n# Inference on Images and Videos\n\nRun the command to test on an Image\n\n```\npython test_images2.py\n```\n\nRun the command to test on a Video\n\n```\npython test_video.py\n```\n\nMake sure to provide right path of video, model file and image file in the script.\n\n# Common Debug\n\n1. Make sure the environment is properly installed\n2. Provide complete path of the model file\n3. Detectron2 installation will take time. Be patient.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fskp-1997%2Fdeepdishtracker","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fskp-1997%2Fdeepdishtracker","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fskp-1997%2Fdeepdishtracker/lists"}