Hello, I am trying to reproduce your validation data on the coco dataset, but I have encountered the following problems

[MMa321]

val: WARNING ⚠️ datasets/images/val2017/000000580757.jpg: ignoring corrupt image/label: Label class 10 exceeds dataset class count 1. Possible class labels are 0-0
val: WARNING ⚠️ datasets/images/val2017/000000581062.jpg: ignoring corrupt image/label: Label class 36 exceeds dataset class count 1. Possible class labels are 0-0
val: WARNING ⚠️ datasets/images/val2017/000000581100.jpg: ignoring corrupt image/label: Label class 23 exceeds dataset class count 1. Possible class labels are 0-0
val: WARNING ⚠️ datasets/images/val2017/000000581206.jpg: ignoring corrupt image/label: Label class 52 exceeds dataset class count 1. Possible class labels are 0-0
val: WARNING ⚠️ datasets/images/val2017/000000581317.jpg: ignoring corrupt image/label: Label class 67 exceeds dataset class count 1. Possible class labels are 0-0
val: WARNING ⚠️ datasets/images/val2017/000000581357.jpg: ignoring corrupt image/label: Label class 36 exceeds dataset class count 1. Possible class labels are 0-0
val: WARNING ⚠️ datasets/images/val2017/000000581482.jpg: ignoring corrupt image/label: Label class 74 exceeds dataset class count 1. Possible class labels are 0-0
val: WARNING ⚠️ datasets/images/val2017/000000581615.jpg: ignoring corrupt image/label: Label class 61 exceeds dataset class count 1. Possible class labels are 0-0
val: WARNING ⚠️ datasets/images/val2017/000000581781.jpg: ignoring corrupt image/label: Label class 46 exceeds dataset class count 1. Possible class labels are 0-0
val: New cache created: datasets/coco/labels/val2017.cache
WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 88, len(boxes) = 177. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 11%|█▏ | 608/5360 [01:11<09:22, 8.44it/s]
Traceback (most recent call last):
File "/home/xtyg/workspace-gm/FastSAM/train_and_validation/val_sa.py", line 4, in
model.val(data="sa.yaml",
File "/home/xtyg/.conda/envs/FastSAM/lib/python3.9/site-packages/ultralytics/engine/model.py", line 503, in val
validator(model=self.model)
File "/home/xtyg/.conda/envs/FastSAM/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home/xtyg/.conda/envs/FastSAM/lib/python3.9/site-packages/ultralytics/engine/validator.py", line 187, in call
self.update_metrics(preds, batch)
File "/home/xtyg/.conda/envs/FastSAM/lib/python3.9/site-packages/ultralytics/models/yolo/segment/val.py", line 111, in update_metrics
pbatch = self._prepare_batch(si, batch)
File "/home/xtyg/.conda/envs/FastSAM/lib/python3.9/site-packages/ultralytics/models/yolo/segment/val.py", line 89, in _prepare_batch
prepared_batch = super()._prepare_batch(si, batch)
File "/home/xtyg/.conda/envs/FastSAM/lib/python3.9/site-packages/ultralytics/models/yolo/detect/val.py", line 99, in _prepare_batch
cls = batch["cls"][idx].squeeze(-1)
IndexError: The shape of the mask [1] at index 0 does not match the shape of the indexed tensor [0, 1] at index 0