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I have read the README carefully. 我已经仔细阅读了README上的操作指引。
I want to train my custom dataset, and I have read the tutorials for training your custom data carefully and organize my dataset correctly; (FYI: We recommand you to apply the config files of xx_finetune.py.) 我想训练自定义数据集,我已经仔细阅读了训练自定义数据的教程,以及按照正确的目录结构存放数据集。(FYI: 我们推荐使用xx_finetune.py等配置文件训练自定义数据集。)
I have pulled the latest code of main branch to run again and the problem still existed. 我已经拉取了主分支上最新的代码,重新运行之后,问题仍不能解决。
Search before asking
I have searched the YOLOv6 issues and found no similar questions.
Question
I finetuned YOLOv6-n model using Caltech-UCSD Birds-200-2011 dataset (https://www.vision.caltech.edu/datasets/cub_200_2011/). However, when I run validation on the trained model using the command:
python tools/eval.py --weights best_ckpt.pt --data data/CUB.yaml --task val --device 0 > testing_log.txt
I get the following results:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.332
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.387
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.368
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.332
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.876
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.886
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.886
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.886
I also checked the annotations file automatically created by YOLO and noticed the areas of the bounding boxes are very large in my dataset. For example:
{"area": 21315.98712932455, "bbox": [115.00002443790436, 84.0000612437725, 146.0000455379486, 145.99986630678177]
Is getting -1 normal for medium and small size areas since the birds cover a large portion of all images in my dataset?
thanks
Additional
No response
The text was updated successfully, but these errors were encountered:
Before Asking
I have read the README carefully. 我已经仔细阅读了README上的操作指引。
I want to train my custom dataset, and I have read the tutorials for training your custom data carefully and organize my dataset correctly; (FYI: We recommand you to apply the config files of xx_finetune.py.) 我想训练自定义数据集,我已经仔细阅读了训练自定义数据的教程,以及按照正确的目录结构存放数据集。(FYI: 我们推荐使用xx_finetune.py等配置文件训练自定义数据集。)
I have pulled the latest code of main branch to run again and the problem still existed. 我已经拉取了主分支上最新的代码,重新运行之后,问题仍不能解决。
Search before asking
Question
I finetuned YOLOv6-n model using Caltech-UCSD Birds-200-2011 dataset (https://www.vision.caltech.edu/datasets/cub_200_2011/). However, when I run validation on the trained model using the command:
python tools/eval.py --weights best_ckpt.pt --data data/CUB.yaml --task val --device 0 > testing_log.txt
I get the following results:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.332
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.387
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.368
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.332
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.876
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.886
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.886
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.886
I also checked the annotations file automatically created by YOLO and noticed the areas of the bounding boxes are very large in my dataset. For example:
{"area": 21315.98712932455, "bbox": [115.00002443790436, 84.0000612437725, 146.0000455379486, 145.99986630678177]
Is getting -1 normal for medium and small size areas since the birds cover a large portion of all images in my dataset?
thanks
Additional
No response
The text was updated successfully, but these errors were encountered: