Deep learning to recognize and count green leafhoppers
We used the model x of YOLO v5, keeping the hyperparameters by default, and annotated 24 images of a subset of 81 tiles with one class of objects of interest. Sixteen images were used for train and eight for validation, respecting the recommended 30% with a split 20–10% between train and validation....
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| Định dạng: | BB |
| Ngôn ngữ: | en_US |
| Thông tin xuất bản: |
2023
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| Chủ đề: | |
| Truy cập trực tuyến: | http://tailieuso.tlu.edu.vn/handle/DHTL/12850 |
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| Tóm tắt: | We used the model x of YOLO v5, keeping the hyperparameters by default, and annotated 24 images of a subset of 81 tiles with one class of objects of interest. Sixteen images were used for train and eight for validation, respecting the recommended 30% with a split 20–10% between train and validation. The train was done once and took a few hours (23.2 h for 448 iterations in a laptop equipped with dual Core Intel i7-10750H processor, 16 GB SDRAM and an NVIDIA GeForce RTX 2060), but the resulting weights can be used to detect the same objects of interest in the future on any similar image (Fig. 1), with a processing time of 2500 ms for each image. |
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