Transfer Learning on Animal Pose Estimation Using YoloV8 and FineTuning
Abstract
Kemajuan dalam teknologi pengolahan citra dan kecerdasan buatan telah membuka peluang baru dalam analisis citra, terutama dalam konteks estimasi pose hewan. Penelitian ini bertujuan menggabungkan keunggulan YOLOV8 dalam deteksi objek dengan akurasi estimasi pose hewan melalui pendekatan transfer learning. Dengan melakukan finetuning pada YOLOV8 menggunakan dataset khusus untuk estimasi pose hewan, penelitian ini berupaya meningkatkan kemampuan model dalam mengenali dan menentukan posisi berbagai bagian tubuh hewan dengan lebih tepat. Suksesnya penelitian ini diharapkan dapat memberikan kontribusi pada pengembangan estimasi pose hewan, membuka peluang dalam pengelolaan kesehatan hewan, studi perilaku hewan, dan aplikasi lain yang membutuhkan analisis citra yang kompleks. Namun, penelitian ini memiliki batasan, termasuk fokus eksklusif pada estimasi pose hewan melalui teknik transfer learning dan fine-tuningg.
Kata Kunci: Stanford Dog Dataset, YOLOV8, finetuning, transfer learning.
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