AssertionError: Image Not Found ../dataset/images/train/4501.jpeg

[Al-Razi-KR]

Input:

python3 train.py --img 640 --batch 16 --epochs 5 --data ./data/dataset.yaml --cfg ./models/yolov5s.yaml --weights weights/yolov5s.pt

Output:

Unable to init server: Could not connect: Connection refused
Unable to init server: Could not connect: Connection refused

(train.py:19670): Gdk-CRITICAL **: 18:33:23.890: gdk_cursor_new_for_display: assertion 'GDK_IS_DISPLAY (display)' failed
Apex recommended for faster mixed precision training: https://github.com/NVIDIA/apex
{'lr0': 0.01, 'momentum': 0.937, 'weight_decay': 0.0005, 'giou': 0.05, 'cls': 0.58, 'cls_pw': 1.0, 'obj': 1.0, 'obj_pw': 1.0, 'iou_t': 0.2, 'anchor_t': 4.0, 'fl_gamma': 0.0, 'hsv_h': 0.014, 'hsv_s': 0.68, 'hsv_v': 0.36, 'degrees': 0.0, 'translate': 0.0, 'scale': 0.5, 'shear': 0.0}
Your branch is behind 'origin/master' by 1 commit, and can be fast-forwarded.
  (use "git pull" to update your local branch)

Namespace(adam=False, batch_size=16, bucket='', cache_images=False, cfg='./models/yolov5s.yaml', data='./data/dataset.yaml', device='', epochs=5, evolve=False, img_size=[640], multi_scale=False, name='', noautoanchor=False, nosave=False, notest=False, rect=False, resume=False, single_cls=False, weights='weights/yolov5s.pt')
Using CUDA device0 _CudaDeviceProperties(name='GeForce RTX 2080 Ti', total_memory=11019MB)

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/

              from  n    params  module                                  arguments
  0             -1  1      3520  models.common.Focus                     [3, 32, 3]
  1             -1  1     18560  models.common.Conv                      [32, 64, 3, 2]
  2             -1  1     19904  models.common.BottleneckCSP             [64, 64, 1]
  3             -1  1     73984  models.common.Conv                      [64, 128, 3, 2]
  4             -1  1    161152  models.common.BottleneckCSP             [128, 128, 3]
  5             -1  1    295424  models.common.Conv                      [128, 256, 3, 2]
  6             -1  1    641792  models.common.BottleneckCSP             [256, 256, 3]
  7             -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]
  8             -1  1    656896  models.common.SPP                       [512, 512, [5, 9, 13]]
  9             -1  1   1248768  models.common.BottleneckCSP             [512, 512, 1, False]
 10             -1  1    131584  models.common.Conv                      [512, 256, 1, 1]
 11             -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']
 12        [-1, 6]  1         0  models.common.Concat                    [1]
 13             -1  1    378624  models.common.BottleneckCSP             [512, 256, 1, False]
 14             -1  1     33024  models.common.Conv                      [256, 128, 1, 1]
 15             -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']
 16        [-1, 4]  1         0  models.common.Concat                    [1]
 17             -1  1     95104  models.common.BottleneckCSP             [256, 128, 1, False]
 18             -1  1      3483  torch.nn.modules.conv.Conv2d            [128, 27, 1, 1]
 19             -2  1    147712  models.common.Conv                      [128, 128, 3, 2]
 20       [-1, 14]  1         0  models.common.Concat                    [1]
 21             -1  1    313088  models.common.BottleneckCSP             [256, 256, 1, False]
 22             -1  1      6939  torch.nn.modules.conv.Conv2d            [256, 27, 1, 1]
 23             -2  1    590336  models.common.Conv                      [256, 256, 3, 2]
 24       [-1, 10]  1         0  models.common.Concat                    [1]
 25             -1  1   1248768  models.common.BottleneckCSP             [512, 512, 1, False]
 26             -1  1     13851  torch.nn.modules.conv.Conv2d            [512, 27, 1, 1]
 27   [-1, 22, 18]  1         0  models.yolo.Detect                      [4, [[116, 90, 156, 198, 373, 326], [30, 61, 62, 45, 59, 119], [10, 13, 16, 30, 33, 23]]]
Model Summary: 191 layers, 7.26318e+06 parameters, 7.26318e+06 gradients

Optimizer groups: 62 .bias, 70 conv.weight, 59 other
Caching labels ../dataset/labels/train.npy (8582 found, 0 missing, 0 empty, 674 duplicate, for 8582 images): 100%|████| 8582/8582 [00:00<00:00, 24748.73it/s]
Caching labels ../dataset/labels/val.npy (1958 found, 0 missing, 0 empty, 135 duplicate, for 1958 images): 100%|██████| 1958/1958 [00:00<00:00, 25395.42it/s]

Analyzing anchors... Best Possible Recall (BPR) = 0.9977
Image sizes 640 train, 640 test
Using 1 dataloader workers
Starting training for 5 epochs...

     Epoch   gpu_mem      GIoU       obj       cls     total   targets  img_size
       0/4     4.72G    0.1325   0.03671   0.05608    0.2252        60       640:   2%|▊                                    | 12/537 [00:07<03:35,  2.44it/s]Traceback (most recent call last):
  File "train.py", line 407, in <module>
    train(hyp)
  File "train.py", line 237, in train
    for i, (imgs, targets, paths, _) in pbar:  # batch -------------------------------------------------------------
  File "/usr/local/lib/python3.6/dist-packages/tqdm/std.py", line 1081, in __iter__
    for obj in iterable:
  File "/home/fyp2020s1/.local/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 345, in __next__
    data = self._next_data()
  File "/home/fyp2020s1/.local/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 856, in _next_data
    return self._process_data(data)
  File "/home/fyp2020s1/.local/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 881, in _process_data
    data.reraise()
  File "/home/fyp2020s1/.local/lib/python3.6/site-packages/torch/_utils.py", line 395, in reraise
    raise self.exc_type(msg)
AssertionError: Caught AssertionError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/home/fyp2020s1/.local/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
    data = fetcher.fetch(index)
  File "/home/fyp2020s1/.local/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/fyp2020s1/.local/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/home/fyp2020s1/YoLo_v5/yolov5/utils/datasets.py", line 446, in __getitem__
    img, labels = load_mosaic(self, index)
  File "/home/fyp2020s1/YoLo_v5/yolov5/utils/datasets.py", line 573, in load_mosaic
    img, _, (h, w) = load_image(self, index)
  File "/home/fyp2020s1/YoLo_v5/yolov5/utils/datasets.py", line 534, in load_image
    assert img is not None, 'Image Not Found ' + path
AssertionError: Image Not Found ../dataset/images/train/4501.jpeg

       0/4     4.72G    0.1325   0.03671   0.05608    0.2252        60       640:   2%|▊ 

[glenn-jocher]

@Al-Razi-KR your custom dataset is not correct, you have missing images.

[Al-Razi-KR]

No, I checked all the images are inside.

[fuddyduddy]

I just encounter the same problem at just. The image is in the train folder as well. Turns out it is because the origin file is .gif. I changed the format with online converter then everything is fine again.

[zhongxu-Sun]

No, I checked all the images are inside.

Hello, did you solve it? I encountered the same problem as you. The picture does exist！

[Al-Razi-KR]

No, I checked all the images are inside.

Hello, did you solve it? I encountered the same problem as you. The picture does exist！

Yes, one of my image was in Gif format. Yolov5 only support a few formats which are mentioned in their documentary. Check your image format using PIL. The cause of my issue was I renamed all the images as jpeg without.

[zhongxu-Sun]

What you mean is that there are pictures in other illegal formats in the data set, not the one that was reported as an error, because the format of the picture that could not be found is correct and it also exists.

…

------------------&nbsp;原始邮件&nbsp;------------------ 发件人: "ultralytics/yolov5" <notifications@github.com&gt;; 发送时间:&nbsp;2021年1月25日(星期一) 晚上6:01 收件人:&nbsp;"ultralytics/yolov5"<yolov5@noreply.github.com&gt;; 抄送:&nbsp;"孙中旭"<2043593085@qq.com&gt;;"Comment"<comment@noreply.github.com&gt;; 主题:&nbsp;Re: [ultralytics/yolov5] AssertionError: Image Not Found ../dataset/images/train/4501.jpeg (#195) No, I checked all the images are inside. Hello, did you solve it? I encountered the same problem as you. The picture does exist！ Yes, one of my image was in Gif format. Yolov5 only support a few formats which are mentioned in their documentary. Check your image format using PIL. The cause of my issue was I renamed all the images as jpeg without. — You are receiving this because you commented. Reply to this email directly, view it on GitHub, or unsubscribe.

[Al-Razi-KR]

What you mean is that there are pictures in other illegal formats in the data set, not the one that was reported as an error, because the format of the picture that could not be found is correct and it also exists.

------------------ 原始邮件 ------------------
发件人: "ultralytics/yolov5" <notifications@github.com>;
发送时间: 2021年1月25日(星期一) 晚上6:01
收件人: "ultralytics/yolov5"<yolov5@noreply.github.com>;
抄送: "孙中旭"<2043593085@qq.com>;"Comment"<comment@noreply.github.com>;
主题: Re: [ultralytics/yolov5] AssertionError: Image Not Found ../dataset/images/train/4501.jpeg (#195)

No, I checked all the images are inside.

Hello, did you solve it? I encountered the same problem as you. The picture does exist！

Yes, one of my image was in Gif format. Yolov5 only support a few formats which are mentioned in their documentary. Check your image format using PIL. The cause of my issue was I renamed all the images as jpeg without.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub, or unsubscribe.

Try:

filename = r"../dataset/images/train/4501.jpeg"
img = Image. open(filename)
print( img. format)
```

[glenn-jocher]

Supported data formats are:

yolov5/utils/datasets.py

Lines 26 to 29 in a41d910

	# Parameters
	help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
	img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng'] # acceptable image suffixes
	vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes

[zhongxu-Sun]

Try:

filename = r"../dataset/images/train/4501.jpeg"
img = Image. open(filename)
print( img. format)

Thanks, indeed, it has been resolved！

[zhongxu-Sun]

Supported data formats are:

yolov5/utils/datasets.py

Lines 26 to 29 in a41d910

	# Parameters
	help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
	img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng'] # acceptable image suffixes
	vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes

Thanks, i got it, it's solved

[glenn-jocher]

@zhongxu-Sun @Al-Razi-KR I was reviewing this. Our intention is to catch corrupted images/labels before training ever starts as part of the label caching process, so I think we may want to add additional checks for this failure mode.

The proper workflow is that dataloader checks flag the corrupted image/label and neglect to include it in the training image list, but there would be no errors and training would proceed smoothly (ignoring the corrupted image).