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| Main Author: | |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.03340 |
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| _version_ | 1866914632233385984 |
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| author | Vajjarapu, Dheeraj |
| author_facet | Vajjarapu, Dheeraj |
| contents | This paper introduces the CowStallNumbers dataset, a collection of images extracted from videos focusing on cow teats, designed to advance the field of cow stall number detection. The dataset comprises 1042 training images and 261 test images, featuring stall numbers ranging from 0 to 60. To enhance the dataset, we performed fine-tuning on a YOLO model and applied data augmentation techniques, including random crop, center crop, and random rotation. The experimental outcomes demonstrate a notable 95.4\% accuracy in recognizing stall numbers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03340 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Classifying cow stall numbers using YOLO Vajjarapu, Dheeraj Computer Vision and Pattern Recognition This paper introduces the CowStallNumbers dataset, a collection of images extracted from videos focusing on cow teats, designed to advance the field of cow stall number detection. The dataset comprises 1042 training images and 261 test images, featuring stall numbers ranging from 0 to 60. To enhance the dataset, we performed fine-tuning on a YOLO model and applied data augmentation techniques, including random crop, center crop, and random rotation. The experimental outcomes demonstrate a notable 95.4\% accuracy in recognizing stall numbers. |
| title | Classifying cow stall numbers using YOLO |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.03340 |