{"id":26939141,"url":"https://github.com/david-lazaro-fernandez/hackmty2024","last_synced_at":"2026-02-13T06:34:15.168Z","repository":{"id":257289094,"uuid":"857457897","full_name":"David-Lazaro-Fernandez/HackMty2024","owner":"David-Lazaro-Fernandez","description":"Softtek wants to buy this","archived":false,"fork":false,"pushed_at":"2024-09-15T18:14:14.000Z","size":86165,"stargazers_count":2,"open_issues_count":10,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-20T00:43:52.164Z","etag":null,"topics":["digital-twins","python","streamlit","tracking-by-detection","yolov8"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/David-Lazaro-Fernandez.png","metadata":{"files":{"readme":"README copy.md","changelog":null,"contributing":"CONTRIBUTING.md","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":"2024-09-14T17:58:04.000Z","updated_at":"2025-02-06T05:21:03.000Z","dependencies_parsed_at":"2024-09-15T19:37:30.159Z","dependency_job_id":null,"html_url":"https://github.com/David-Lazaro-Fernandez/HackMty2024","commit_stats":null,"previous_names":["david-lazaro-fernandez/hackmty2024"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/David-Lazaro-Fernandez/HackMty2024","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/David-Lazaro-Fernandez%2FHackMty2024","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/David-Lazaro-Fernandez%2FHackMty2024/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/David-Lazaro-Fernandez%2FHackMty2024/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/David-Lazaro-Fernandez%2FHackMty2024/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/David-Lazaro-Fernandez","download_url":"https://codeload.github.com/David-Lazaro-Fernandez/HackMty2024/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/David-Lazaro-Fernandez%2FHackMty2024/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":262266254,"owners_count":23284664,"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":["digital-twins","python","streamlit","tracking-by-detection","yolov8"],"created_at":"2025-04-02T14:16:11.128Z","updated_at":"2026-02-13T06:34:10.125Z","avatar_url":"https://github.com/David-Lazaro-Fernandez.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\u003cdiv align=\"center\"\u003e\n\n# YOLOv8 DeepSORT Streamlit\n\n  \u003cp\u003e\n    \u003ca align=\"center\" href=\"https://ultralytics.com/yolov8\" target=\"_blank\"\u003e\n      \u003cimg width=\"50%\" src=\"images/banner-yolov8.png\"\u003e\u003c/a\u003e\n  \u003c/p\u003e\n\n\u003cbr\u003e\n\n\u003cdiv\u003e\n    \u003ca href=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml\"\u003e\u003cimg src=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg\" alt=\"Ultralytics CI\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://zenodo.org/badge/latestdoi/264818686\"\u003e\u003cimg src=\"https://zenodo.org/badge/264818686.svg\" alt=\"YOLOv8 Citation\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://hub.docker.com/r/ultralytics/ultralytics\"\u003e\u003cimg src=\"https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker\" alt=\"Docker Pulls\"\u003e\u003c/a\u003e\n    \u003cbr\u003e\n    \u003ca href=\"https://console.paperspace.com/github/ultralytics/ultralytics\"\u003e\u003cimg src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"/\u003e\u003c/a\u003e\n    \u003ca href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://www.kaggle.com/ultralytics/yolov8\"\u003e\u003cimg src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"\u003e\u003c/a\u003e\n  \u003c/div\u003e\n  \u003cbr\u003e\n\u003c/div\u003e\n\n## Introduction\nThis repository supply a user-friendly interactive interface for [YOLOv8](https://github.com/ultralytics/ultralytics) with Object Tracking and Counting capability. The interface is powered by [Streamlit](https://github.com/streamlit/streamlit).\n\n## Features\n- Feature1: Object detection task.\n- Feature2: Multiple detection models. `yolov8n`, `yolov8s`, `yolov8m`, `yolov8l`, `yolov8x`\n- Feature3: Multiple input formats. `Image`, `Video`, `Webcam`\n- Feature4: Multiple Object Tracking and Counting.\n\n## Run online\nYou can use [This](https://monemati-yolov8-deepsort-streamlit-app-et5bli.streamlit.app/) link to try an online version on Streamlit.   \n\n## Installation\n### Create a virtual environment\n```commandline\n# create\npython -m venv yolov8-mot-streamlit\n\n# activate\nsource yolov8-mot-streamlit/bin/activate\n```\n\n### Clone repository\n```commandline\ngit clone https://github.com/monemati/YOLOv8-DeepSORT-Streamlit.git\ncd YOLOv8-DeepSORT-Streamlit\n```\n\n### Install packages\n```commandline\n# Streamlit dependencies\npip install streamlit\n\n# YOLOv8 dependecies\npip install -e '.[dev]'\n```\n### Download Pre-trained YOLOv8 Detection Weights\nCreate a directory named `weights` and create a subdirectory named `detection` and save the downloaded YOLOv8 object detection weights inside this directory. The weight files can be downloaded from the table below.\n\n| Model                                                                                | size\u003cbr\u003e\u003csup\u003e(pixels) | mAP\u003csup\u003eval\u003cbr\u003e50-95 | Speed\u003cbr\u003e\u003csup\u003eCPU ONNX\u003cbr\u003e(ms) | Speed\u003cbr\u003e\u003csup\u003eA100 TensorRT\u003cbr\u003e(ms) | params\u003cbr\u003e\u003csup\u003e(M) | FLOPs\u003cbr\u003e\u003csup\u003e(B) |\n| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |\n| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640                   | 37.3                 | 80.4                           | 0.99                                | 3.2                | 8.7               |\n| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640                   | 44.9                 | 128.4                          | 1.20                                | 11.2               | 28.6              |\n| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640                   | 50.2                 | 234.7                          | 1.83                                | 25.9               | 78.9              |\n| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640                   | 52.9                 | 375.2                          | 2.39                                | 43.7               | 165.2             |\n| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640                   | 53.9                 | 479.1                          | 3.53                                | 68.2               | 257.8             |\n\n\n## Run\n```commandline\nstreamlit run app.py\n```\nThen will start the Streamlit server and open your web browser to the default Streamlit page automatically.\nFor Object Counting, you can choose \"Video\" from \"Select Source\" combo box and use \"test3.mp4\" inside videos folder as an example.\n\n## Result\n\n![alt text](images/YOLOv8-DeepSort-Streamlit-Counting.jpg \"YOLOv8 DeepSort Streamlit Counting\")\n  \n## Acknowledgement\n- https://github.com/ultralytics/ultralytics\n- https://github.com/streamlit/streamlit\n- https://github.com/ZQPei/deep_sort_pytorch\n- https://github.com/JackDance/YOLOv8-streamlit-app\n- https://github.com/MuhammadMoinFaisal/YOLOv8-DeepSORT-Object-Tracking\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdavid-lazaro-fernandez%2Fhackmty2024","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdavid-lazaro-fernandez%2Fhackmty2024","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdavid-lazaro-fernandez%2Fhackmty2024/lists"}