[X] I have read the README carefully. 我已经仔细阅读了README上的操作指引。
[X] 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。)
[X] 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。
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[X] I have searched the YOLOv6 issues and found no similar questions.
Question
I have a problem related to validation using the costum dataset that I have with the pretrained coco model. where the dataset class that I have consists of 7 classes with the order ["vehicle", "motorcycle", "public transport", "becak", "truck", "person", "traffic sign"].
how to solve the difference, because the results of precision, recall, and map are very low.
I would be very grateful if you can help solve this problem.
Hi @Mirshal , In this case, I suggest that you modify the class_id in label files for your custom dataset , in order to directly and correctly apply coco pretrained model for validation.
Before Asking
[X] I have read the README carefully. 我已经仔细阅读了README上的操作指引。
[X] 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。)
[X] 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 have a problem related to validation using the costum dataset that I have with the pretrained coco model. where the dataset class that I have consists of 7 classes with the order ["vehicle", "motorcycle", "public transport", "becak", "truck", "person", "traffic sign"].
While the pretrained coco model consists of 80 classes in the order ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow','elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee','skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush' ]
how to solve the difference, because the results of precision, recall, and map are very low. I would be very grateful if you can help solve this problem.
Additional
No response