{"id":25063005,"url":"https://github.com/utkarsh251106/smart-inventory","last_synced_at":"2026-05-06T01:34:37.654Z","repository":{"id":275835927,"uuid":"927338132","full_name":"Utkarsh251106/Smart-Inventory","owner":"Utkarsh251106","description":"A Computer Vision project using YOLO11n for detecting and counting fruits and vegetables in an image or a video stream. It sends Telegram alerts if the item count drops below 5 for more than 5 seconds.","archived":false,"fork":false,"pushed_at":"2025-05-06T13:21:16.000Z","size":485945,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-27T23:39:26.470Z","etag":null,"topics":["artificial-intelligence","computer-vision","deep-learning","machine-learning","objec","opencv","python","ultralytics","yolo11-detection"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/Utkarsh251106.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":"docs/SECURITY.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-02-04T19:43:01.000Z","updated_at":"2025-05-06T13:21:19.000Z","dependencies_parsed_at":"2025-05-06T11:27:59.162Z","dependency_job_id":"595e35ca-a247-4e66-ba46-5a24b5a489ad","html_url":"https://github.com/Utkarsh251106/Smart-Inventory","commit_stats":null,"previous_names":["utkarsh251106/smart-inventory"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Utkarsh251106/Smart-Inventory","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Utkarsh251106%2FSmart-Inventory","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Utkarsh251106%2FSmart-Inventory/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Utkarsh251106%2FSmart-Inventory/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Utkarsh251106%2FSmart-Inventory/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Utkarsh251106","download_url":"https://codeload.github.com/Utkarsh251106/Smart-Inventory/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Utkarsh251106%2FSmart-Inventory/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":281361398,"owners_count":26487881,"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":"2025-10-27T02:00:05.855Z","response_time":61,"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":["artificial-intelligence","computer-vision","deep-learning","machine-learning","objec","opencv","python","ultralytics","yolo11-detection"],"created_at":"2025-02-06T17:35:17.848Z","updated_at":"2025-10-27T23:39:28.156Z","avatar_url":"https://github.com/Utkarsh251106.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Description\nThis project utilizes YOLOv11n for detecting and counting vegtables and fruits in an image or a video streams. It processes the video to identify and count the number of items in each frame, alerting the user via Telegram if the tomato count drops below 5 for more than 5 seconds. The project is designed to handle real-time video input and provide continuous monitoring of the detected object, sending notifications when needed.\n\n# How to run it?\n### Step 1: Clone the Repository:\n  \n```bash\ngit clone https://github.com/Utkarsh251106/Smart-Inventory\n```\n### Step 2: Create a conda environment:\n  \n```bash\nconda create -n venv python=3.12.7 -y\nconda activate venv\n```\n\n### Step 3: Install the requirements:\n  \n```bash\npip install -r requirements.txt\n```\n### Step 4: To find the model:\nFollow this path to get the model -\u003e model/best.pt\n\n### Step 5: To run the code(for Fruit-and-Vegetable-detection files):\n  To run the code\n```bash\n# Start the Jupyter Notebook environment using the command\njupyter notebook\n```\n#### Run your Code_for_images.ipynb file for detection in an image in the notebooks folder\n#### Run your Code_for_video.ipynb file for detections in a video in the notebooks folder\n\n### Step 6(Optional): To run the streamlit file(present in the fruit-veg-detector folder):\n  To run the code\n```bash\n# Start the Jupyter Notebook environment using the command\nstreamlit run app.py\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Futkarsh251106%2Fsmart-inventory","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Futkarsh251106%2Fsmart-inventory","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Futkarsh251106%2Fsmart-inventory/lists"}