{"id":2493,"date":"2021-09-22T09:00:00","date_gmt":"2021-09-22T01:00:00","guid":{"rendered":"https:\/\/blog.ailia.ai\/uncategorized\/vehicleattributerecognitionbarrier-machine-learning-model-to-detect-vehicle-attributes\/"},"modified":"2025-05-20T16:54:08","modified_gmt":"2025-05-20T08:54:08","slug":"vehicleattributerecognitionbarrier-machine-learning-model-to-detect-vehicle-attributes","status":"publish","type":"post","link":"https:\/\/blog.ailia.ai\/en\/tips-en\/vehicleattributerecognitionbarrier-machine-learning-model-to-detect-vehicle-attributes\/","title":{"rendered":"VehicleAttributeRecognitionBarrier : A Machine Learning Model for Detecting Car Attributes"},"content":{"rendered":"\n<h3 class=\"wp-block-heading\" id=\"e831\"><strong>Overview<\/strong><\/h3>\n\n\n\n<p id=\"b48e\"><em>VehicleAttributeRecognitionBarrier\u00a0<\/em>is a machine learning model developed by\u00a0<em>Intel\u00a0<\/em>to identify the type and the color of a car.<\/p>\n\n\n\n<p><a href=\"https:\/\/github.com\/openvinotoolkit\/open_model_zoo\/blob\/master\/models\/intel\/vehicle-attributes-recognition-barrier-0042\/README.md?source=post_page-----ee26d1a3e00b--------------------------------\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><a href=\"https:\/\/github.com\/openvinotoolkit\/open_model_zoo\/blob\/master\/models\/intel\/vehicle-attributes-recognition-barrier-0042\/README.md\" target=\"_blank\" rel=\"noreferrer noopener\">open_model_zoo\/README.md<\/a><\/p>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"1280\" height=\"720\" src=\"https:\/\/blog.ailia.ai\/wp-content\/uploads\/image-34.jpg\" alt=\"\" class=\"wp-image-250\"\/><figcaption class=\"wp-element-caption\">Source:\u00a0<a href=\"https:\/\/pixabay.com\/ja\/videos\/%E8%AD%A6%E5%AF%9F%E3%81%AE%E8%BB%8A-%E5%B8%82-%E3%83%88%E3%83%A9%E3%83%95%E3%82%A3%E3%83%83%E3%82%AF-6095\/\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/pixabay.com\/ja\/videos\/%E8%AD%A6%E5%AF%9F%E3%81%AE%E8%BB%8A-%E5%B8%82-%E3%83%88%E3%83%A9%E3%83%95%E3%82%A3%E3%83%83%E3%82%AF-6095\/<\/a><\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"1cab\"><strong>Architecture<\/strong><\/h3>\n\n\n\n<p id=\"1c79\">The model takes a frontal image of a car and outputs attributes.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"226\" height=\"327\" src=\"https:\/\/blog.ailia.ai\/wp-content\/uploads\/image-32.png\" alt=\"\" class=\"wp-image-248\"\/><figcaption class=\"wp-element-caption\">Source: <a href=\"https:\/\/github.com\/openvinotoolkit\/open_model_zoo\/blob\/master\/models\/intel\/vehicle-attributes-recognition-barrier-0042\/README.md\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/github.com\/openvinotoolkit\/open_model_zoo\/blob\/master\/models\/intel\/vehicle-attributes-recognition-barrier-0042\/README.md<\/a><\/figcaption><\/figure>\n\n\n\n<p id=\"0be3\">There are 7 categories for colors and 4 categories for vehicle types, with accuracies of respectively 82.71% and 87.34%<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"287\" height=\"692\" src=\"https:\/\/blog.ailia.ai\/wp-content\/uploads\/image-33.png\" alt=\"\" class=\"wp-image-249\"\/><figcaption class=\"wp-element-caption\">Source: <a href=\"https:\/\/github.com\/openvinotoolkit\/open_model_zoo\/blob\/master\/models\/intel\/vehicle-attributes-recognition-barrier-0042\/README.md\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/github.com\/openvinotoolkit\/open_model_zoo\/blob\/master\/models\/intel\/vehicle-attributes-recognition-barrier-0042\/README.md<\/a><\/figcaption><\/figure>\n\n\n\n<p id=\"3c60\">The model architecture has a\u00a0<em>ResNet<\/em>-like structure. The input is a (1,72,72,3) image and the output is a (1,7) vector of color probabilities and a (1,4) vector of car types.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:1244\/1*SfIDv2PJcxUrABHjYeo72g.png\" alt=\"\"\/><figcaption class=\"wp-element-caption\">Source: <a href=\"https:\/\/netron.app\/?url=https%3A%2F%2Fstorage.googleapis.com%2Failia-models%2Fvehicle-attributes-recognition-barrier%2Fvehicle-attributes-recognition-barrier-0042.onnx.prototxt\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/netron.app\/?url=https:\/\/storage.googleapis.com\/ailia-models\/vehicle-attributes-recognition-barrier\/vehicle-attributes-recognition-barrier-0042.onnx.prototxt<\/a><\/figcaption><\/figure>\n\n\n\n<p id=\"627d\">As a constraint, you need to give the image of the front of the car with less than 50% occlusion.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"530\" height=\"410\" src=\"https:\/\/blog.ailia.ai\/wp-content\/uploads\/image-31.png\" alt=\"\" class=\"wp-image-247\"\/><figcaption class=\"wp-element-caption\">Source: <a href=\"https:\/\/github.com\/openvinotoolkit\/open_model_zoo\/blob\/master\/models\/intel\/vehicle-attributes-recognition-barrier-0042\/README.md\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/github.com\/openvinotoolkit\/open_model_zoo\/blob\/master\/models\/intel\/vehicle-attributes-recognition-barrier-0042\/README.md<\/a><\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"f7db\"><strong>Usage<\/strong><\/h3>\n\n\n\n<p id=\"1936\">You can use\u00a0<em>VehicleAttributeRecognitionBarrier\u00a0<\/em>with ailia SDK using the following command. After detecting the car with\u00a0<a href=\"https:\/\/medium.com\/axinc-ai\/yolov3-a-machine-learning-model-to-detect-the-position-and-type-of-an-object-60f1c18f8107\">YOLOv3<\/a>\u00a0for any video, the attributes will be inferred.<\/p>\n\n\n\n<p><code>$ python3 vehicle-attributes-recognition-barrier.py -v input.mp4<\/code><\/p>\n\n\n\n<p id=\"a1fd\">Here is an example output.<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"ailia MODELS : VehicleAttributeRecognitionBarrier\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/YolZAJR_3zQ?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p><a href=\"https:\/\/github.com\/axinc-ai\/ailia-models\/tree\/master\/vehicle_recognition\/vehicle-attributes-recognition-barrier?source=post_page-----ee26d1a3e00b--------------------------------\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><a href=\"https:\/\/github.com\/axinc-ai\/ailia-models\/tree\/master\/vehicle_recognition\/vehicle-attributes-recognition-barrier\" target=\"_blank\" rel=\"noreferrer noopener\">ailia-models\/vehicle_recognition\/vehicle-attributes-recognition-barrier<\/a><\/p>\n\n\n\n<p id=\"9c5c\"><a href=\"https:\/\/axinc.jp\/en\/\" rel=\"noreferrer noopener\" target=\"_blank\">ax Inc.<\/a>&nbsp;has developed&nbsp;<a href=\"https:\/\/ailia.jp\/en\/\" rel=\"noreferrer noopener\" target=\"_blank\">ailia SDK<\/a>, which enables cross-platform, GPU-based rapid inference.<\/p>\n\n\n\n<p id=\"9c5c\">ax Inc. provides a wide range of services from consulting and model creation, to the development of AI-based applications and SDKs. Feel free to&nbsp;<a href=\"https:\/\/axinc.jp\/en\/\" rel=\"noreferrer noopener\" target=\"_blank\">contact us<\/a>&nbsp;for any inquiry.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Overview VehicleAttributeRecognitionBarrier\u00a0is a machine learning model developed by\u00a0Intel\u00a0to identify the type and the color of a car. open_model_zoo\/README.md Architecture The model takes a frontal image of a car and outputs attributes. There are 7 categories for colors and 4 categories for vehicle types, with accuracies of respectively 82.71% and 87.34% The model architecture has a\u00a0ResNet-like structure. The input is a (1,72,72,3) image and the output is a (1,7) vector of color probabilities and a (1,4) vector of car types. As a constraint, you need to give the image of the front of the car with less than 50% occlusion. Usage You can use\u00a0VehicleAttributeRecognitionBarrier\u00a0with ailia SDK using the following command. After [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":2436,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[255],"tags":[266],"class_list":["post-2493","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tips-en","tag-ailiamodels-en"],"acf":[],"_links":{"self":[{"href":"https:\/\/blog.ailia.ai\/en\/wp-json\/wp\/v2\/posts\/2493","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.ailia.ai\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.ailia.ai\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.ailia.ai\/en\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.ailia.ai\/en\/wp-json\/wp\/v2\/comments?post=2493"}],"version-history":[{"count":1,"href":"https:\/\/blog.ailia.ai\/en\/wp-json\/wp\/v2\/posts\/2493\/revisions"}],"predecessor-version":[{"id":2495,"href":"https:\/\/blog.ailia.ai\/en\/wp-json\/wp\/v2\/posts\/2493\/revisions\/2495"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.ailia.ai\/en\/wp-json\/wp\/v2\/media\/2436"}],"wp:attachment":[{"href":"https:\/\/blog.ailia.ai\/en\/wp-json\/wp\/v2\/media?parent=2493"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.ailia.ai\/en\/wp-json\/wp\/v2\/categories?post=2493"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.ailia.ai\/en\/wp-json\/wp\/v2\/tags?post=2493"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}