How to convert from json format with Polygon labels to YOLOv7 instance segmentation Without Roboflow? #12560
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@Yuanchihwei make sure you are using the latest code from the |
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I meet with similar problem in yolov9. |
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It seems like there's a mismatch in the dimensions of your arrays during the concatenation process in the if segments.ndim != segments2.ndim:
# Reshape or handle arrays to match dimensions
# Example reshaping to 3D if they are not:
segments = segments.reshape(-1, segments.shape[0], segments.shape[1])
segments2 = segments2.reshape(-1, segments2.shape[0], segments2.shape[1])
segments = np.concatenate((segments, segments2), 0)This code is just a starting point; adjust as necessary based on your actual data structure. Good luck! 🚀 |
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Yes,I do it. I have converted from json format with Polygon labels to YOLOv7 instance segmentation. The solution to this problem is to use hyp.scratch-high.yaml but set mixup to 0.0
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主题: Re: [ultralytics/yolov5] How to convert from json format with Polygon labels to YOLOv7 instance segmentation Without Roboflow? (Issue #12560)
It seems like there's a mismatch in the dimensions of your arrays during the concatenation process in the mixup function. This often occurs when the data isn't uniformly structured. Ensure all your segments arrays have the same number of dimensions before concatenation. A quick fix could be checking and reshaping arrays if necessary before the np.concatenate call. For example:
if segments.ndim != segments2.ndim: # Reshape or handle arrays to match dimensions # Example reshaping to 3D if they are not: segments = segments.reshape(-1, segments.shape[0], segments.shape[1]) segments2 = segments2.reshape(-1, segments2.shape[0], segments2.shape[1]) segments = np.concatenate((segments, segments2), 0)
This code is just a starting point; adjust as necessary based on your actual data structure. Good luck! 🚀
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Great to hear you've found a solution by adjusting the |
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Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size
7/299 18.3G 0.06648 0.05584 0.03083 0.0004405 92 640: 100%|██████████| 260/260 [41:52<00:00, 9.66s/it]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████| 33/33 [02:55<00:00, 5.32s/
all 1038 4626 0.677 0.127 0.106 0.0427 0.673 0.119 0.101 0.037
Traceback (most recent call last):
File "D:\yolov7-u7\yolov7-u7\seg\segment\train.py", line 681, in
main(opt)
File "D:\yolov7-u7\yolov7-u7\seg\segment\train.py", line 577, in main
train(opt.hyp, opt, device, callbacks)
File "D:\yolov7-u7\yolov7-u7\seg\segment\train.py", line 295, in train
for i, (imgs, targets, paths, _, masks) in pbar: # batch ------------------------------------------------------
File "D:\anaconda\Lib\site-packages\tqdm\std.py", line 1178, in iter
for obj in iterable:
File "D:\yolov7-u7\yolov7-u7\seg\utils\dataloaders.py", line 171, in iter
yield next(self.iterator)
^^^^^^^^^^^^^^^^^^^
File "D:\anaconda\Lib\site-packages\torch\utils\data\dataloader.py", line 630, in next
data = self._next_data()
^^^^^^^^^^^^^^^^^
File "D:\anaconda\Lib\site-packages\torch\utils\data\dataloader.py", line 1345, in _next_data
return self._process_data(data)
^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda\Lib\site-packages\torch\utils\data\dataloader.py", line 1371, in _process_data
data.reraise()
File "D:\anaconda\Lib\site-packages\torch_utils.py", line 694, in reraise
raise exception
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "D:\anaconda\Lib\site-packages\torch\utils\data_utils\worker.py", line 308, in _worker_loop
data = fetcher.fetch(index)
^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda\Lib\site-packages\torch\utils\data_utils\fetch.py", line 51, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda\Lib\site-packages\torch\utils\data_utils\fetch.py", line 51, in
data = [self.dataset[idx] for idx in possibly_batched_index]
~~~~~~~~~~~~^^^^^
File "D:\yolov7-u7\yolov7-u7\seg\utils\segment\dataloaders.py", line 116, in getitem
img, labels, segments = mixup(img, labels, segments, *self.load_mosaic(random.randint(0, self.n - 1)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\yolov7-u7\yolov7-u7\seg\utils\segment\augmentations.py", line 21, in mixup
segments = np.concatenate((segments, segments2), 0)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<array_function internals>", line 180, in concatenate
ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 3 dimension(s) and the array at index 1 has 1 dimension(s)
******I did try to train with Yolov7-seg with "python segment/train.py --data data/custom.yaml --batch 16 --weights '' --cfg yolov7-seg.yaml --epochs 300 --name yolov7-seg --img 640 --hyp hyp.scratch-high.yaml", but it appears error above, i did search articals but I can't found how to convert from json format with Polygon labels to YOLOv7 instance segmentation Without Roboflow? Beside, format issue, any other problem could cause this error?
Source: https://github.com/WongKinYiu/yolov7/tree/u7/seg
Current, my label text is converted using Labelme2YOLO.
Source: https://github.com/rooneysh/Labelme2YOLO
I am coding beginner, Please top help me, thanks**.****
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