{"id":20858149,"url":"https://github.com/arkasarkar19/car-detection-using-yolo","last_synced_at":"2025-10-24T21:19:16.070Z","repository":{"id":84065802,"uuid":"272548487","full_name":"ArkaSarkar19/Car-Detection-using-YOLO","owner":"ArkaSarkar19","description":"Refer Readme.md","archived":false,"fork":false,"pushed_at":"2020-06-16T09:02:35.000Z","size":61748,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-19T07:15:14.272Z","etag":null,"topics":["anchor-boxes","bounding-boxes","car-detection-neural-network","compter-vision","dimension","encoding","machine-learning","non-maximum-suppression","yolo","yolo-architecture"],"latest_commit_sha":null,"homepage":"","language":"Python","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/ArkaSarkar19.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":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-06-15T21:33:11.000Z","updated_at":"2021-03-27T12:17:18.000Z","dependencies_parsed_at":null,"dependency_job_id":"456f340b-b0ea-42b4-8972-cf03389b623f","html_url":"https://github.com/ArkaSarkar19/Car-Detection-using-YOLO","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ArkaSarkar19%2FCar-Detection-using-YOLO","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ArkaSarkar19%2FCar-Detection-using-YOLO/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ArkaSarkar19%2FCar-Detection-using-YOLO/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ArkaSarkar19%2FCar-Detection-using-YOLO/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ArkaSarkar19","download_url":"https://codeload.github.com/ArkaSarkar19/Car-Detection-using-YOLO/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243230108,"owners_count":20257643,"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":["anchor-boxes","bounding-boxes","car-detection-neural-network","compter-vision","dimension","encoding","machine-learning","non-maximum-suppression","yolo","yolo-architecture"],"created_at":"2024-11-18T04:44:52.810Z","updated_at":"2025-10-24T21:19:11.031Z","avatar_url":"https://github.com/ArkaSarkar19.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Car-Detection-using-YOLO\n\nYOLO (\"you only look once\") is a popular algoritm because it achieves high accuracy while also being able to run in real-time. This algorithm \"only looks once\" at the image in the sense that it requires only one forward propagation pass through the network to make predictions. After non-max suppression, it then outputs recognized objects together with the bounding boxes. \u003c/br\u003e\n## YOLO model Architecture \n\n ![YOLO](readme_images/model_architecture.png?raw=true)\n \n \u003c/br\u003e\n \n### Model details\n* Inputs and outputs   \u003c/br\u003e\n  * The **input** is a batch of images, and each image has the shape (m, 608, 608, 3) \u003c/br\u003e\n  * The **output** is a list of bounding boxes along with the recognized classes. Each bounding box is represented by 6 numbers (pc,bx,by,bh,bw,c)(pc,bx,by,bh,bw,c) as explained above. If you expand cc into an 80-dimensional vector, each bounding box is then represented by 85 numbers. \u003c/br\u003e\n\n* Anchor Boxes \u003c/br\u003e\n  * Anchor boxes are chosen by exploring the training data to choose reasonable height/width ratios that represent the different classes. For this assignment, 5 anchor boxes were chosen for you (to cover the 80 classes), and stored in the file './model_data/yolo_anchors.txt' \u003c/br\u003e\n  * The dimension for anchor boxes is the second to last dimension in the encoding: (m,nH,nW,anchors,classes)(m,nH,nW,anchors,classes). \u003c/br\u003e\n  * The YOLO architecture is: IMAGE (m, 608, 608, 3) -\u003e DEEP CNN -\u003e ENCODING (m, 19, 19, 5, 85). \u003c/br\u003e\n  \n### NOTE\n* To generate yolo.h5 file go to this [link](https://github.com/allanzelener/YAD2K). Place that in **model_data** folder. \u003c/br\u003e\n* Input images are in the **images** directory and the correcponding output images are in the **out** directory. \u003c/br\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farkasarkar19%2Fcar-detection-using-yolo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farkasarkar19%2Fcar-detection-using-yolo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farkasarkar19%2Fcar-detection-using-yolo/lists"}