File name creates corrupted files

Community Help
1

After creating a dataset with roboflow and use it to train a YOLOv11 object detection model, I have a problem with corrupted files. Recently, I extracted pictures from a video with a very long file name. Previously, I had no problem with corrupted files.

Is there a way to change the file names from the annotated images? I want to avoid to label all images again.

The pattern of the file name from the corrupted images:

0164-bN-004-001-pN-PPLgPeanutGreen29-pH-29-RPM-41-V-15-S-None-pT-Runner-bP-REMOVED-T-0-fC-0-mD-500_mp4-0000_jpg.rf.a5cea801e9f78adc03eb8ca69511d2d8.jpg

The warning message while training the model:
Ultralytics 8.3.26  Python-3.12.3 torch-2.3.0+cu121 CUDA:0 (NVIDIA GeForce RTX 4090, 24564MiB)
engine\trainer: task=detect, mode=train, model=yolov10m.pt, data=data.yaml, epochs=60, time=None, patience=100, batch=-1, imgsz=[640, 360], save=True, save_period=-1, cache=False, device=0, workers=8, project=Peanut_Seed_Meter, name=YOLOv10m.pt_V10 - DS_V262, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=True, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.2, hsv_s=0.8, hsv_v=0.6, degrees=45, translate=0.1, scale=0.5, shear=10.0, perspective=0.0, flipud=0.1, fliplr=0.5, bgr=0.1, mosaic=1.0, mixup=0.2, copy_paste=0.2, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=Peanut_Seed_Meter\YOLOv10m.pt_V10 - DS_V262
Overriding model.yaml nc=80 with nc=2

                   from  n    params  module                                       arguments                     
  0                  -1  1      1392  ultralytics.nn.modules.conv.Conv             [3, 48, 3, 2]                 
  1                  -1  1     41664  ultralytics.nn.modules.conv.Conv             [48, 96, 3, 2]                
  2                  -1  2    111360  ultralytics.nn.modules.block.C2f             [96, 96, 2, True]             
  3                  -1  1    166272  ultralytics.nn.modules.conv.Conv             [96, 192, 3, 2]               
  4                  -1  4    813312  ultralytics.nn.modules.block.C2f             [192, 192, 4, True]           
  5                  -1  1     78720  ultralytics.nn.modules.block.SCDown          [192, 384, 3, 2]              
  6                  -1  4   3248640  ultralytics.nn.modules.block.C2f             [384, 384, 4, True]           
  7                  -1  1    228672  ultralytics.nn.modules.block.SCDown          [384, 576, 3, 2]              
  8                  -1  2   1689984  ultralytics.nn.modules.block.C2fCIB          [576, 576, 2, True]           
  9                  -1  1    831168  ultralytics.nn.modules.block.SPPF            [576, 576, 5]                 
 10                  -1  1   1253088  ultralytics.nn.modules.block.PSA             [576, 576]                    
 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 13                  -1  2   1993728  ultralytics.nn.modules.block.C2f             [960, 384, 2]                 
 14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 16                  -1  2    517632  ultralytics.nn.modules.block.C2f             [576, 192, 2]                 
 17                  -1  1    332160  ultralytics.nn.modules.conv.Conv             [192, 192, 3, 2]              
 18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
...
    16486444        1024        12.484           129           nan       (16, 3, 640, 640)                    list
    16486444        2047        24.000           544           nan       (32, 3, 640, 640)                    list
    16486444        4094        46.561          5786           nan       (64, 3, 640, 640)                    list
AutoBatch: Using batch-size 10 for CUDA:0 18.26G/23.99G (76%) 
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...
train: Scanning C:\Users\mb70069\OneDrive - University of Georgia\101 - Peanut_seed_meter\300 - Testdata & Dataset\000 - Datasets\V26_PSM-yolov11\train\labels.cache... 5511 images, 51 backgrounds, 693 corrupt: 100%|██████████| 6204/6204 [00:00<?, ?it/s]
train: WARNING  C:\Users\mb70069\OneDrive - University of Georgia\101 - Peanut_seed_meter\300 - Testdata & Dataset\000 - Datasets\V26_PSM-yolov11\train\images\0164-bN-004-001-pN-PPLgPeanutGreen29-pH-29-RPM-41-V-15-S-None-pT-Runner-bP-REMOVED-T-0-fC-0-mD-500_mp4-0000_jpg.rf.a5cea801e9f78adc03eb8ca69511d2d8.jpg: ignoring corrupt image/label: [Errno 2] No such file or directory: 'C:\\Users\\mb70069\\OneDrive - University of Georgia\\101 - Peanut_seed_meter\\300 - Testdata & Dataset\\000 - Datasets\\V26_PSM-yolov11\\train\\images\\0164-bN-004-001-pN-PPLgPeanutGreen29-pH-29-RPM-41-V-15-S-None-pT-Runner-bP-REMOVED-T-0-fC-0-mD-500_mp4-0000_jpg.rf.a5cea801e9f78adc03eb8ca69511d2d8.jpg'
train: WARNING  C:\Users\mb70069\OneDrive - University of Georgia\101 - Peanut_seed_meter\300 - Testdata & Dataset\000 - Datasets\V26_PSM-yolov11\train\images\0164-bN-004-001-pN-PPLgPeanutGreen29-pH-29-RPM-41-V-15-S-None-pT-Runner-bP-REMOVED-T-0-fC-0-mD-500_mp4-0000_jpg.rf.c964a712631ea1e80c009324bfcc6417.jpg: ignoring corrupt image/label: [Errno 2] No such file or directory: 'C:\\Users\\mb70069\\OneDrive - University of Georgia\\101 - Peanut_seed_meter\\300 - Testdata & Dataset\\000 - Datasets\\V26_PSM-yolov11\\train\\images\\0164-bN-004-001-pN-PPLgPeanutGreen29-pH-29-RPM-41-V-15-S-None-pT-Runner-bP-REMOVED-T-0-fC-0-mD-500_mp4-0000_jpg.rf.c964a712631ea1e80c009324bfcc6417.jpg'
train: WARNING  C:\Users\mb70069\OneDrive - University of Georgia\101 - Peanut_seed_meter\300 - Testdata & Dataset\000 - Datasets\V26_PSM-yolov11\train\images\0164-bN-004-001-pN-PPLgPeanutGreen29-pH-29-RPM-41-V-15-S-None-pT-Runner-bP-REMOVED-T-0-fC-0-mD-500_mp4-0000_jpg.rf.f6b3d1a78fc8bcc9fab3d511397deac5.jpg: ignoring corrupt image/label: [Errno 2] No such file or directory: 'C:\\Users\\mb70069\\OneDrive - University of Georgia\\101 - Peanut_seed_meter\\300 - Testdata & Dataset\\000 - Datasets\\V26_PSM-yolov11\\train\\images\\0164-bN-004-001-pN-PPLgPeanutGreen29-pH-29-RPM-41-V-15-S-None-pT-Runner-bP-REMOVED-T-0-fC-0-mD-500_mp4-0000_jpg.rf.f6b3d1a78fc8bcc9fab3d511397deac5.jpg'
train: WARNING  C:\Users\mb70069\OneDrive - University of Georgia\101 - Peanut_seed_meter\300 - Testdata & Dataset\000 - Datasets\V26_PSM-yolov11\train\images\0164-bN-004-001-pN-PPLgPeanutGreen29-pH-29-RPM-41-V-15-S-None-pT-Runner-bP-REMOVED-T-0-fC-0-mD-500_mp4-0002_jpg.rf.22981f316d0ceb7eb27c76fa2e804d21.jpg: ignoring corrupt image/label: [Errno 2] No such file or directory: 'C:\\Users\\mb70069\\OneDrive - University of Georgia\\101 - Peanut_seed_meter\\300 - Testdata & Dataset\\000 - Datasets\\V26_PSM-yolov11\\train\\images\\0164-bN-004-001-pN-PPLgPeanutGreen29-pH-29-RPM-41-V-15-S-None-pT-Runner-bP-REMOVED-T-0-fC-0-mD-500_mp4-0002_jpg.rf.22981f316d0ceb7eb27c76fa2e804d21.jpg'
train: WARNING  C:\Users\mb70069\OneDrive - University of Georgia\101 - Peanut_seed_meter\300 - Testdata & Dataset\000 - Datasets\V26_PSM-yolov11\train\images\0164-bN-004-001-pN-PPLgPeanutGreen29-pH-29-RPM-41-V-15-S-None-pT-Runner-bP-REMOVED-T-0-fC-0-mD-500_mp4-0002_jpg.rf.34149ab5f066294e0ad8409c8d7c5250.jpg: ignoring corrupt image/label: [Errno 2] No such file or directory: 'C:\\Users\\mb70069\\OneDrive - University of Georgia\\101 - Peanut_seed_meter\\300 - Testdata & Dataset\\000 - Datasets\\V26_PSM-yolov11\\train\\images\\0164-bN-004-001-pN-PPLgPeanutGreen29-pH-29-RPM-41-V-15-S-None-pT-Runner-bP-REMOVED-T-0-fC-0-mD-500_mp4-0002_jpg.rf.34149ab5f066294e0ad8409c8d7c5250.jpg'

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2

Hi – have you tried training YOLOv11 inside Roboflow?

We’re unable to troubleshoot issues in training runs outside our platform, but can help if you’re using Roboflow’s train options.

3

I just changed the file names of two images and the corresponding labels by hand. This action reduced the number of corrupt files by two.
Thus the problem is the long file name. Is there a way to change the file name from images in a roboflow.com dataset?

4

I don’t believe so, sorry. You may need to export the images and then rename them from there.

Again, this should be natively supported if you train within Roboflow.

5

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