{"id":33107,"date":"2025-03-20T05:27:42","date_gmt":"2025-03-20T04:27:42","guid":{"rendered":"https:\/\/www.graviton.at\/letterswaplibrary\/question-for-improving-custom-floating-trash-dataset-for-object-detection-model\/"},"modified":"2025-03-20T05:27:42","modified_gmt":"2025-03-20T04:27:42","slug":"question-for-improving-custom-floating-trash-dataset-for-object-detection-model","status":"publish","type":"post","link":"https:\/\/www.graviton.at\/letterswaplibrary\/question-for-improving-custom-floating-trash-dataset-for-object-detection-model\/","title":{"rendered":"Question For Improving Custom Floating Trash Dataset For Object Detection Model"},"content":{"rendered":"<p><!-- SC_OFF --><\/p>\n<div class=\"md\">\n<p>I have a dataset of 10k images for an object detection model designed to detect and predict floating trash. This model will be deployed in marine environments, such as lakes, oceans, etc. I am trying to upgrade my dataset by gathering images from different sources and datasets. I&#8217;m wondering if adding images of trash, like plastic and glass, from non-marine environments (such as land-based or non-floating images) will affect my model&#8217;s precision. Since the model will primarily be used on a boat in water, could this introduce any potential problems? Any suggestions or tips would be greatly appreciated.<\/p>\n<\/div>\n<p><!-- SC_ON -->   submitted by   <a href=\"https:\/\/www.reddit.com\/user\/Fit-Information6080\"> \/u\/Fit-Information6080 <\/a> <br \/> <span><a href=\"https:\/\/www.reddit.com\/r\/datasets\/comments\/1jfg1k2\/question_for_improving_custom_floating_trash\/\">[link]<\/a><\/span>   <span><a href=\"https:\/\/www.reddit.com\/r\/datasets\/comments\/1jfg1k2\/question_for_improving_custom_floating_trash\/\">[comments]<\/a><\/span><\/p><div class='watch-action'><div class='watch-position align-right'><div class='action-like'><a class='lbg-style1 like-33107 jlk' href='javascript:void(0)' data-task='like' data-post_id='33107' data-nonce='614a020375' rel='nofollow'><img class='wti-pixel' src='https:\/\/www.graviton.at\/letterswaplibrary\/wp-content\/plugins\/wti-like-post\/images\/pixel.gif' title='Like' \/><span class='lc-33107 lc'>0<\/span><\/a><\/div><\/div> <div class='status-33107 status align-right'><\/div><\/div><div class='wti-clear'><\/div>","protected":false},"excerpt":{"rendered":"<p>I have a dataset of 10k images for an object detection model designed to detect and predict&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[85],"tags":[],"class_list":["post-33107","post","type-post","status-publish","format-standard","hentry","category-datatards","wpcat-85-id"],"_links":{"self":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/33107","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/comments?post=33107"}],"version-history":[{"count":0,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/33107\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/media?parent=33107"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/categories?post=33107"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/tags?post=33107"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}