raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for YOLO:
Missing key(s) in state_dict: "model.model.0.conv.weight", "model.model.0.bn.weight", "model.model.0.bn.bias", "model.model.0.bn.running_mean", "model.model.0.bn.running_var", "model.model.1.conv.weight", "model.model.1.bn.weight", "model.model.1.bn.bias", "model.model.1.bn.running_mean", "model.model.1.bn.running_var", "model.model.2.cv1.conv.weight", "model.model.2.cv1.bn.weight", "model.model.2.cv1.bn.bias", "model.model.2.cv1.bn.running_mean", "model.model.2.cv1.bn.running_var", "model.model.2.cv2.conv.weight", "model.model.2.cv2.bn.weight", "model.model.2.cv2.bn.bias", "model.model.2.cv2.bn.running_mean", "model.model.2.cv2.bn.running_var", "model.model.2.m.0.cv1.conv.weight", "model.model.2.m.0.cv1.bn.weight", "model.model.2.m.0.cv1.bn.bias", "model.model.2.m.0.cv1.bn.running_mean", "model.model.2.m.0.cv1.bn.running_var", "model.model.2.m.0.cv2.conv.weight", "model.model.2.m.0.cv2.bn.weight", "model.model.2.m.0.cv2.bn.bias", "model.model.2.m.0.cv2.bn.running_mean", "model.model.2.m.0.cv2.bn.running_var", "model.model.3.conv.weight", "model.model.3.bn.weight", "model.model.3.bn.bias", "model.model.3.bn.running_mean", "model.model.3.bn.running_var", "model.model.4.cv1.conv.weight", "model.model.4.cv1.bn.weight", "model.model.4.cv1.bn.bias", "model.model.4.cv1.bn.running_mean", "model.model.4.cv1.bn.running_var", "model.model.4.cv2.conv.weight", "model.model.4.cv2.bn.weight", "model.model.4.cv2.bn.bias", "model.model.4.cv2.bn.running_mean", "model.model.4.cv2.bn.running_var", "model.model.4.m.0.cv1.conv.weight", "model.model.4.m.0.cv1.bn.weight", "model.model.4.m.0.cv1.bn.bias", "model.model.4.m.0.cv1.bn.running_mean", "model.model.4.m.0.cv1.bn.running_var", "model.model.4.m.0.cv2.conv.weight", "model.model.4.m.0.cv2.bn.weight", "model.model.4.m.0.cv2.bn.bias", "model.model.4.m.0.cv2.bn.running_mean", "model.model.4.m.0.cv2.bn.running_var", "model.model.5.conv.weight", "model.model.5.bn.weight", "model.model.5.bn.bias", "model.model.5.bn.running_mean", "model.model.5.bn.running_var", "model.model.6.cv1.conv.weight", "model.model.6.cv1.bn.weight", "model.model.6.cv1.bn.bias", "model.model.6.cv1.bn.running_mean", "model.model.6.cv1.bn.running_var", "model.model.6.cv2.conv.weight", "model.model.6.cv2.bn.weight", "model.model.6.cv2.bn.bias", "model.model.6.cv2.bn.running_mean", "model.model.6.cv2.bn.running_var", "model.model.6.m.0.cv1.conv.weight", "model.model.6.m.0.cv1.bn.weight", "model.model.6.m.0.cv1.bn.bias", "model.model.6.m.0.cv1.bn.running_mean", "model.model.6.m.0.cv1.bn.running_var", "model.model.6.m.0.cv2.conv.weight", "model.model.6.m.0.cv2.bn.weight", "model.model.6.m.0.cv2.bn.bias", "model.model.6.m.0.cv2.bn.running_mean", "model.model.6.m.0.cv2.bn.running_var", "model.model.6.m.0.cv3.conv.weight", "model.model.6.m.0.cv3.bn.weight", "model.model.6.m.0.cv3.bn.bias", "model.model.6.m.0.cv3.bn.running_mean", "model.model.6.m.0.cv3.bn.running_var", "model.model.6.m.0.m.0.cv1.conv.weight", "model.model.6.m.0.m.0.cv1.bn.weight", "model.model.6.m.0.m.0.cv1.bn.bias", "model.model.6.m.0.m.0.cv1.bn.running_mean", "model.model.6.m.0.m.0.cv1.bn.running_var", "model.model.6.m.0.m.0.cv2.conv.weight", "model.model.6.m.0.m.0.cv2.bn.weight", "model.model.6.m.0.m.0.cv2.bn.bias", "model.model.6.m.0.m.0.cv2.bn.running_mean", "model.model.6.m.0.m.0.cv2.bn.running_var", "model.model.6.m.0.m.1.cv1.conv.weight", "model.model.6.m.0.m.1.cv1.bn.weight", "model.model.6.m.0.m.1.cv1.bn.bias", "model.model.6.m.0.m.1.cv1.bn.running_mean", "model.model.6.m.0.m.1.cv1.bn.running_var", "model.model.6.m.0.m.1.cv2.conv.weight", "model.model.6.m.0.m.1.cv2.bn.weight", "model.model.6.m.0.m.1.cv2.bn.bias", "model.model.6.m.0.m.1.cv2.bn.running_mean", "model.model.6.m.0.m.1.cv2.bn.running_var", "model.model.7.conv.weight", "model.model.7.bn.weight", "model.model.7.bn.bias", "model.model.7.bn.running_mean", "model.model.7.bn.running_var", "model.model.8.cv1.conv.weight", "model.model.8.cv1.bn.weight", "model.model.8.cv1.bn.bias", "model.model.8.cv1.bn.running_mean", "model.model.8.cv1.bn.running_var", "model.model.8.cv2.conv.weight", "model.model.8.cv2.bn.weight", "model.model.8.cv2.bn.bias", "model.model.8.cv2.bn.running_mean", "model.model.8.cv2.bn.running_var", "model.model.8.m.0.cv1.conv.weight", "model.model.8.m.0.cv1.bn.weight", "model.model.8.m.0.cv1.bn.bias", "model.model.8.m.0.cv1.bn.running_mean", "model.model.8.m.0.cv1.bn.running_var", "model.model.8.m.0.cv2.conv.weight", "model.model.8.m.0.cv2.bn.weight", "model.model.8.m.0.cv2.bn.bias", "model.model.8.m.0.cv2.bn.running_mean", "model.model.8.m.0.cv2.bn.running_var", "model.model.8.m.0.cv3.conv.weight", "model.model.8.m.0.cv3.bn.weight", "model.model.8.m.0.cv3.bn.bias", "model.model.8.m.0.cv3.bn.running_mean", "model.model.8.m.0.cv3.bn.running_var", "model.model.8.m.0.m.0.cv1.conv.weight", "model.model.8.m.0.m.0.cv1.bn.weight", "model.model.8.m.0.m.0.cv1.bn.bias", "model.model.8.m.0.m.0.cv1.bn.running_mean", "model.model.8.m.0.m.0.cv1.bn.running_var", "model.model.8.m.0.m.0.cv2.conv.weight", "model.model.8.m.0.m.0.cv2.bn.weight", "model.model.8.m.0.m.0.cv2.bn.bias", "model.model.8.m.0.m.0.cv2.bn.running_mean", "model.model.8.m.0.m.0.cv2.bn.running_var", "model.model.8.m.0.m.1.cv1.conv.weight", "model.model.8.m.0.m.1.cv1.bn.weight", "model.model.8.m.0.m.1.cv1.bn.bias", "model.model.8.m.0.m.1.cv1.bn.running_mean", "model.model.8.m.0.m.1.cv1.bn.running_var", "model.model.8.m.0.m.1.cv2.conv.weight", "model.model.8.m.0.m.1.cv2.bn.weight", "model.model.8.m.0.m.1.cv2.bn.bias", "model.model.8.m.0.m.1.cv2.bn.running_mean", "model.model.8.m.0.m.1.cv2.bn.running_var", "model.model.9.cv1.conv.weight", "model.model.9.cv1.bn.weight", "model.model.9.cv1.bn.bias", "model.model.9.cv1.bn.running_mean", "model.model.9.cv1.bn.running_var", "model.model.9.cv2.conv.weight", "model.model.9.cv2.bn.weight", "model.model.9.cv2.bn.bias", "model.model.9.cv2.bn.running_mean", "model.model.9.cv2.bn.running_var", "model.model.10.cv1.conv.weight", "model.model.10.cv1.bn.weight", "model.model.10.cv1.bn.bias", "model.model.10.cv1.bn.running_mean", "model.model.10.cv1.bn.running_var", "model.model.10.cv2.conv.weight", "model.model.10.cv2.bn.weight", "model.model.10.cv2.bn.bias", "model.model.10.cv2.bn.running_mean", "model.model.10.cv2.bn.running_var", "model.model.10.m.0.attn.qkv.conv.weight", "model.model.10.m.0.attn.qkv.bn.weight", "model.model.10.m.0.attn.qkv.bn.bias", "model.model.10.m.0.attn.qkv.bn.running_mean", "model.model.10.m.0.attn.qkv.bn.running_var", "model.model.10.m.0.attn.proj.conv.weight", "model.model.10.m.0.attn.proj.bn.weight", "model.model.10.m.0.attn.proj.bn.bias", "model.model.10.m.0.attn.proj.bn.running_mean", "model.model.10.m.0.attn.proj.bn.running_var", "model.model.10.m.0.attn.pe.conv.weight", "model.model.10.m.0.attn.pe.bn.weight", "model.model.10.m.0.attn.pe.bn.bias", "model.model.10.m.0.attn.pe.bn.running_mean", "model.model.10.m.0.attn.pe.bn.running_var", "model.model.10.m.0.ffn.0.conv.weight", "model.model.10.m.0.ffn.0.bn.weight", "model.model.10.m.0.ffn.0.bn.bias", "model.model.10.m.0.ffn.0.bn.running_mean", "model.model.10.m.0.ffn.0.bn.running_var", "model.model.10.m.0.ffn.1.conv.weight", "model.model.10.m.0.ffn.1.bn.weight", "model.model.10.m.0.ffn.1.bn.bias", "model.model.10.m.0.ffn.1.bn.running_mean", "model.model.10.m.0.ffn.1.bn.running_var", "model.model.13.cv1.conv.weight", "model.model.13.cv1.bn.weight", "model.model.13.cv1.bn.bias", "model.model.13.cv1.bn.running_mean", "model.model.13.cv1.bn.running_var", "model.model.13.cv2.conv.weight", "model.model.13.cv2.bn.weight", "model.model.13.cv2.bn.bias", "model.model.13.cv2.bn.running_mean", "model.model.13.cv2.bn.running_var", "model.model.13.m.0.cv1.conv.weight", "model.model.13.m.0.cv1.bn.weight", "model.model.13.m.0.cv1.bn.bias", "model.model.13.m.0.cv1.bn.running_mean", "model.model.13.m.0.cv1.bn.running_var", "model.model.13.m.0.cv2.conv.weight", "model.model.13.m.0.cv2.bn.weight", "model.model.13.m.0.cv2.bn.bias", "model.model.13.m.0.cv2.bn.running_mean", "model.model.13.m.0.cv2.bn.running_var", "model.model.16.cv1.conv.weight", "model.model.16.cv1.bn.weight", "model.model.16.cv1.bn.bias", "model.model.16.cv1.bn.running_mean", "model.model.16.cv1.bn.running_var", "model.model.16.cv2.conv.weight", "model.model.16.cv2.bn.weight", "model.model.16.cv2.bn.bias", "model.model.16.cv2.bn.running_mean", "model.model.16.cv2.bn.running_var", "model.model.16.m.0.cv1.conv.weight", "model.model.16.m.0.cv1.bn.weight", "model.model.16.m.0.cv1.bn.bias", "model.model.16.m.0.cv1.bn.running_mean", "model.model.16.m.0.cv1.bn.running_var", "model.model.16.m.0.cv2.conv.weight", "model.model.16.m.0.cv2.bn.weight", "model.model.16.m.0.cv2.bn.bias", "model.model.16.m.0.cv2.bn.running_mean", "model.model.16.m.0.cv2.bn.running_var", "model.model.17.conv.weight", "model.model.17.bn.weight", "model.model.17.bn.bias", "model.model.17.bn.running_mean", "model.model.17.bn.running_var", "model.model.19.cv1.conv.weight", "model.model.19.cv1.bn.weight", "model.model.19.cv1.bn.bias", "model.model.19.cv1.bn.running_mean", "model.model.19.cv1.bn.running_var", "model.model.19.cv2.conv.weight", "model.model.19.cv2.bn.weight", "model.model.19.cv2.bn.bias", "model.model.19.cv2.bn.running_mean", "model.model.19.cv2.bn.running_var", "model.model.19.m.0.cv1.conv.weight", "model.model.19.m.0.cv1.bn.weight", "model.model.19.m.0.cv1.bn.bias", "model.model.19.m.0.cv1.bn.running_mean", "model.model.19.m.0.cv1.bn.running_var", "model.model.19.m.0.cv2.conv.weight", "model.model.19.m.0.cv2.bn.weight", "model.model.19.m.0.cv2.bn.bias", "model.model.19.m.0.cv2.bn.running_mean", "model.model.19.m.0.cv2.bn.running_var", "model.model.20.conv.weight", "model.model.20.bn.weight", "model.model.20.bn.bias", "model.model.20.bn.running_mean", "model.model.20.bn.running_var", "model.model.22.cv1.conv.weight", "model.model.22.cv1.bn.weight", "model.model.22.cv1.bn.bias", "model.model.22.cv1.bn.running_mean", "model.model.22.cv1.bn.running_var", "model.model.22.cv2.conv.weight", "model.model.22.cv2.bn.weight", "model.model.22.cv2.bn.bias", "model.model.22.cv2.bn.running_mean", "model.model.22.cv2.bn.running_var", "model.model.22.m.0.cv1.conv.weight", "model.model.22.m.0.cv1.bn.weight", "model.model.22.m.0.cv1.bn.bias", "model.model.22.m.0.cv1.bn.running_mean", "model.model.22.m.0.cv1.bn.running_var", "model.model.22.m.0.cv2.conv.weight", "model.model.22.m.0.cv2.bn.weight", "model.model.22.m.0.cv2.bn.bias", "model.model.22.m.0.cv2.bn.running_mean", "model.model.22.m.0.cv2.bn.running_var", "model.model.22.m.0.cv3.conv.weight", "model.model.22.m.0.cv3.bn.weight", "model.model.22.m.0.cv3.bn.bias", "model.model.22.m.0.cv3.bn.running_mean", "model.model.22.m.0.cv3.bn.running_var", "model.model.22.m.0.m.0.cv1.conv.weight", "model.model.22.m.0.m.0.cv1.bn.weight", "model.model.22.m.0.m.0.cv1.bn.bias", "model.model.22.m.0.m.0.cv1.bn.running_mean", "model.model.22.m.0.m.0.cv1.bn.running_var", "model.model.22.m.0.m.0.cv2.conv.weight", "model.model.22.m.0.m.0.cv2.bn.weight", "model.model.22.m.0.m.0.cv2.bn.bias", "model.model.22.m.0.m.0.cv2.bn.running_mean", "model.model.22.m.0.m.0.cv2.bn.running_var", "model.model.22.m.0.m.1.cv1.conv.weight", "model.model.22.m.0.m.1.cv1.bn.weight", "model.model.22.m.0.m.1.cv1.bn.bias", "model.model.22.m.0.m.1.cv1.bn.running_mean", "model.model.22.m.0.m.1.cv1.bn.running_var", "model.model.22.m.0.m.1.cv2.conv.weight", "model.model.22.m.0.m.1.cv2.bn.weight", "model.model.22.m.0.m.1.cv2.bn.bias", "model.model.22.m.0.m.1.cv2.bn.running_mean", "model.model.22.m.0.m.1.cv2.bn.running_var", "model.model.23.cv2.0.0.conv.weight", "model.model.23.cv2.0.0.bn.weight", "model.model.23.cv2.0.0.bn.bias", "model.model.23.cv2.0.0.bn.running_mean", "model.model.23.cv2.0.0.bn.running_var", "model.model.23.cv2.0.1.conv.weight", "model.model.23.cv2.0.1.bn.weight", "model.model.23.cv2.0.1.bn.bias", "model.model.23.cv2.0.1.bn.running_mean", "model.model.23.cv2.0.1.bn.running_var", "model.model.23.cv2.0.2.weight", "model.model.23.cv2.0.2.bias", "model.model.23.cv2.1.0.conv.weight", "model.model.23.cv2.1.0.bn.weight", "model.model.23.cv2.1.0.bn.bias", "model.model.23.cv2.1.0.bn.running_mean", "model.model.23.cv2.1.0.bn.running_var", "model.model.23.cv2.1.1.conv.weight", "model.model.23.cv2.1.1.bn.weight", "model.model.23.cv2.1.1.bn.bias", "model.model.23.cv2.1.1.bn.running_mean", "model.model.23.cv2.1.1.bn.running_var", "model.model.23.cv2.1.2.weight", "model.model.23.cv2.1.2.bias", "model.model.23.cv2.2.0.conv.weight", "model.model.23.cv2.2.0.bn.weight", "model.model.23.cv2.2.0.bn.bias", "model.model.23.cv2.2.0.bn.running_mean", "model.model.23.cv2.2.0.bn.running_var", "model.model.23.cv2.2.1.conv.weight", "model.model.23.cv2.2.1.bn.weight", "model.model.23.cv2.2.1.bn.bias", "model.model.23.cv2.2.1.bn.running_mean", "model.model.23.cv2.2.1.bn.running_var", "model.model.23.cv2.2.2.weight", "model.model.23.cv2.2.2.bias", "model.model.23.cv3.0.0.0.conv.weight", "model.model.23.cv3.0.0.0.bn.weight", "model.model.23.cv3.0.0.0.bn.bias", "model.model.23.cv3.0.0.0.bn.running_mean", "model.model.23.cv3.0.0.0.bn.running_var", "model.model.23.cv3.0.0.1.conv.weight", "model.model.23.cv3.0.0.1.bn.weight", "model.model.23.cv3.0.0.1.bn.bias", "model.model.23.cv3.0.0.1.bn.running_mean", "model.model.23.cv3.0.0.1.bn.running_var", "model.model.23.cv3.0.1.0.conv.weight", "model.model.23.cv3.0.1.0.bn.weight", "model.model.23.cv3.0.1.0.bn.bias", "model.model.23.cv3.0.1.0.bn.running_mean", "model.model.23.cv3.0.1.0.bn.running_var", "model.model.23.cv3.0.1.1.conv.weight", "model.model.23.cv3.0.1.1.bn.weight", "model.model.23.cv3.0.1.1.bn.bias", "model.model.23.cv3.0.1.1.bn.running_mean", "model.model.23.cv3.0.1.1.bn.running_var", "model.model.23.cv3.0.2.weight", "model.model.23.cv3.0.2.bias", "model.model.23.cv3.1.0.0.conv.weight", "model.model.23.cv3.1.0.0.bn.weight", "model.model.23.cv3.1.0.0.bn.bias", "model.model.23.cv3.1.0.0.bn.running_mean", "model.model.23.cv3.1.0.0.bn.running_var", "model.model.23.cv3.1.0.1.conv.weight", "model.model.23.cv3.1.0.1.bn.weight", "model.model.23.cv3.1.0.1.bn.bias", "model.model.23.cv3.1.0.1.bn.running_mean", "model.model.23.cv3.1.0.1.bn.running_var", "model.model.23.cv3.1.1.0.conv.weight", "model.model.23.cv3.1.1.0.bn.weight", "model.model.23.cv3.1.1.0.bn.bias", "model.model.23.cv3.1.1.0.bn.running_mean", "model.model.23.cv3.1.1.0.bn.running_var", "model.model.23.cv3.1.1.1.conv.weight", "model.model.23.cv3.1.1.1.bn.weight", "model.model.23.cv3.1.1.1.bn.bias", "model.model.23.cv3.1.1.1.bn.running_mean", "model.model.23.cv3.1.1.1.bn.running_var", "model.model.23.cv3.1.2.weight", "model.model.23.cv3.1.2.bias", "model.model.23.cv3.2.0.0.conv.weight", "model.model.23.cv3.2.0.0.bn.weight", "model.model.23.cv3.2.0.0.bn.bias", "model.model.23.cv3.2.0.0.bn.running_mean", "model.model.23.cv3.2.0.0.bn.running_var", "model.model.23.cv3.2.0.1.conv.weight", "model.model.23.cv3.2.0.1.bn.weight", "model.model.23.cv3.2.0.1.bn.bias", "model.model.23.cv3.2.0.1.bn.running_mean", "model.model.23.cv3.2.0.1.bn.running_var", "model.model.23.cv3.2.1.0.conv.weight", "model.model.23.cv3.2.1.0.bn.weight", "model.model.23.cv3.2.1.0.bn.bias", "model.model.23.cv3.2.1.0.bn.running_mean", "model.model.23.cv3.2.1.0.bn.running_var", "model.model.23.cv3.2.1.1.conv.weight", "model.model.23.cv3.2.1.1.bn.weight", "model.model.23.cv3.2.1.1.bn.bias", "model.model.23.cv3.2.1.1.bn.running_mean", "model.model.23.cv3.2.1.1.bn.running_var", "model.model.23.cv3.2.2.weight", "model.model.23.cv3.2.2.bias", "model.model.23.dfl.conv.weight".
Unexpected key(s) in state_dict: "nc", "scales", "backbone", "head".
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博客主要介绍了model1和model2，还对model2进行了深入探讨，涉及信息技术领域的模型相关内容。
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