truthdotphd

/

vessel-detection

like 1

• "ultralytics.nn.modules.block.C3k",
• "ultralytics.utils.IterableSimpleNamespace",
• "ultralytics.nn.modules.conv.DWConv",
• "torch.FloatStorage",
• "torch.nn.modules.container.Sequential",
• "torch._utils._rebuild_parameter",
• "__builtin__.set",
• "torch.HalfStorage",
• "ultralytics.nn.modules.block.C3k2",
• "torch.nn.modules.container.ModuleList",
• "ultralytics.nn.modules.block.C2PSA",
• "ultralytics.nn.modules.block.SPPF",
• "torch.nn.modules.upsampling.Upsample",
• "ultralytics.utils.loss.BboxLoss",
• "torch.nn.modules.conv.Conv2d",
• "numpy.core.multiarray.scalar",
• "torch.device",
• "ultralytics.nn.modules.head.Detect",
• "ultralytics.nn.modules.block.Attention",
• "torch.nn.modules.linear.Identity",
• "ultralytics.nn.modules.conv.Concat",
• "ultralytics.nn.modules.conv.Conv",
• "torch._utils._rebuild_tensor_v2",
• "torch.nn.modules.activation.SiLU",
• "ultralytics.nn.modules.block.PSABlock",
• "ultralytics.utils.loss.DFLoss",
• "ultralytics.utils.tal.TaskAlignedAssigner",
• "ultralytics.nn.modules.block.Bottleneck",
• "ultralytics.nn.modules.block.DFL",
• "torch.nn.modules.pooling.MaxPool2d",
• "numpy.dtype",
• "_codecs.encode",
• "torch.nn.modules.batchnorm.BatchNorm2d",
• "torch.nn.modules.loss.BCEWithLogitsLoss",
• "ultralytics.nn.tasks.DetectionModel",
• "torch.Size",
• "ultralytics.utils.loss.v8DetectionLoss",
• "collections.OrderedDict",
• "torch.LongStorage"

How to fix it?