Revisi per 10 Maret 2024 07.56 sunting Badak Jawa (bicara \| kontrib) Pengecualian blokir IP, Peninjau, Pengembali revisi 27.802 suntingan Tidak ada ringkasan suntingan Tag: VisualEditor Suntingan perangkat seluler Suntingan peramban seluler Suntingan seluler lanjutan ← Revisi sebelumnya		Revisi per 10 Maret 2024 07.57 sunting balikkan Badak Jawa (bicara \| kontrib) Pengecualian blokir IP, Peninjau, Pengembali revisi 27.802 suntingan Tidak ada ringkasan suntingan Tag: VisualEditor Suntingan perangkat seluler Suntingan peramban seluler Suntingan seluler lanjutan Revisi selanjutnya →
Baris 4: == Sejarah == Tujuan awal dari R-CNN adalah untuk mengambil gambar input dan menghasilkan sekumpulan kotak pembatas sebagai output, di mana setiap kotak pembatas berisi objek dan juga kategori (misalnya mobil atau pejalan kaki) dari objek tersebut. Baru-baru ini, R-CNN telah diperluas untuk melakukan tugas-tugas visi komputer lainnya. Berikut ini adalah beberapa versi R-CNN yang telah dikembangkan. The original goal of R-CNN was to take an input image and produce a set of bounding boxes as output, where each bounding box contains an object and also the category (e.g. car or pedestrian) of the object. More recently, R-CNN has been extended to perform other computer vision tasks. The following covers some of the versions of R-CNN that have been developed. * November 2013: '''R-CNN'''. Given an input image, R-CNN begins by applying a mechanism called Selective Search to extract [[Region of interest\|regions of interest]] (ROI), where each ROI is a rectangle that may represent the boundary of an object in image. Depending on the scenario, there may be as many as two thousand ROIs. After that, each ROI is fed through a neural network to produce output features. For each ROI's output features, a collection of [[support-vector machine]] classifiers is used to determine what type of object (if any) is contained within the ROI.<ref>{{Cite news\|last=Gandhi\|first=Rohith\|url=https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e\|title=R-CNN, Fast R-CNN, Faster R-CNN, YOLO — Object Detection Algorithms\|date=July 9, 2018\|work=Towards Data Science\|access-date=March 12, 2020}}</ref>

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