Label Missing: for images and labels... 203 found, 50 missing, 0 empty, 0 corrupted: 100%

[Rana-Javed]

Scanning '../GuavaDataset/labels/train.cache' for images and labels... 203 found, 50 missing, 0 empty, 0 corrupted: 100%

50 Labels are missing while caching. and results are very low:

Epoch gpu_mem box obj cls total targets img_size
49/49 1.82G 0.08977 0.1288 0 0.2186 107 416: 100% 16/16 [00:02<00:00, 7.06it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 2/2 [00:00<00:00, 2.75it/s]
all 26 131 0.147 0.191 0.0793 0.0181

[github-actions]

👋 Hello @Rana-Javed, thank you for your interest in 🚀 YOLOv5! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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[glenn-jocher]

@Rana-Javed 50 missing indicates that 50 of your images lack labels. If this was not intentional then you have a problem in your dataset.

See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data to get started.

[github-actions]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.

[abuelgasimsaadeldin]

@glenn-jocher, "missing" indicates no text file at all, or an empty text file, or either? Also from what I understand from the Tips for Best Training Results it is good to have a few "missing" labels to reduce model's FP, is that correct?

[glenn-jocher]

@abuelgasimsaadeldin missing indicates no label file was found for an image, i.e. these will be treated as background images.

[MHassanlatif]

@glenn-jocher, "missing" indicates no text file at all, or an empty text file, or either? Also from what I understand from the Tips for Best Training Results it is good to have a few "missing" labels to reduce model's FP, is that correct?

And what does (8933 found, 0 missing, 7736 empty, 0 duplicate, for 21041 images)
7736 empty represent here?

[glenn-jocher]

@MHassanlatif empty images without any labels