Copyright
©The Author(s) 2024.
World J Gastroenterol. Dec 28, 2024; 30(48): 5111-5129
Published online Dec 28, 2024. doi: 10.3748/wjg.v30.i48.5111
Published online Dec 28, 2024. doi: 10.3748/wjg.v30.i48.5111
Table 2 Training results of the YOLOv8 versions using our dataset
| Model | Parameter (M) | mAP50 | mAP50:95 | GFLOPS | FPS |
| YOLOv8n | 2.87 | 86.0 | 66.2 | 8.2 | 400.00 |
| YOLOv8s | 10.62 | 86.1 | 67.6 | 28.7 | 283.03 |
| YOLOv8m | 24.67 | 88.6 | 68.3 | 79.1 | 189.47 |
| YOLOv8 L | 41.62 | 85.3 | 67.1 | 165.5 | 149.25 |
| YOLOv8x | 65.01 | 83.2 | 64.1 | 258.2 | 114.94 |
- Citation: Xiao ZG, Chen XQ, Zhang D, Li XY, Dai WX, Liang WH. Image detection method for multi-category lesions in wireless capsule endoscopy based on deep learning models. World J Gastroenterol 2024; 30(48): 5111-5129
- URL: https://www.wjgnet.com/1007-9327/full/v30/i48/5111.htm
- DOI: https://dx.doi.org/10.3748/wjg.v30.i48.5111