An active learning model to classify animal species in Hong Kong
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917621014724608 |
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| author | Lamb, Gareth Lo, Ching Hei Wu, Jin Lee, Calvin K. F. |
| author_facet | Lamb, Gareth Lo, Ching Hei Wu, Jin Lee, Calvin K. F. |
| contents | Camera traps are used by ecologists globally as an efficient and non-invasive method to monitor animals. While it is time-consuming to manually label the collected images, recent advances in deep learning and computer vision has made it possible to automating this process [1]. A major obstacle to this is the generalisability of these models when applying these images to independently collected data from other parts of the world [2]. Here, we use a deep active learning workflow [3], and train a model that is applicable to camera trap images collected in Hong Kong. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15675 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An active learning model to classify animal species in Hong Kong Lamb, Gareth Lo, Ching Hei Wu, Jin Lee, Calvin K. F. Computer Vision and Pattern Recognition Camera traps are used by ecologists globally as an efficient and non-invasive method to monitor animals. While it is time-consuming to manually label the collected images, recent advances in deep learning and computer vision has made it possible to automating this process [1]. A major obstacle to this is the generalisability of these models when applying these images to independently collected data from other parts of the world [2]. Here, we use a deep active learning workflow [3], and train a model that is applicable to camera trap images collected in Hong Kong. |
| title | An active learning model to classify animal species in Hong Kong |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.15675 |