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Using MobileNet-V3 and ConvNeXt_v2 to train the model, modifying only number_classes ,topk and using the default parameters, the inference results remained accuracy/ top1:51.9751 .This is not the case with networks such as resnet and resnext.
The confusion matrix results are as follows:
Data set picture size is 224*224, a total of 4 classes
I tried to modify the loss function to CrossEntropyLoss ,simplify data preprocessing operations in train_pipeline and cancel the augments operation during model training, but still failed to solve the problem. Please kindly ask experts for help.
分支
main 分支 (mmpretrain 版本)
描述该错误
Using MobileNet-V3 and ConvNeXt_v2 to train the model, modifying only number_classes ,topk and using the default parameters, the inference results remained accuracy/ top1:51.9751 .This is not the case with networks such as resnet and resnext.
The confusion matrix results are as follows:
Data set picture size is 224*224, a total of 4 classes
I tried to modify the loss function to CrossEntropyLoss ,simplify data preprocessing operations in train_pipeline and cancel the augments operation during model training, but still failed to solve the problem. Please kindly ask experts for help.
环境信息
{'sys.platform': 'linux',
'Python': '3.8.0 (default, Nov 6 2019, 21:49:08) [GCC 7.3.0]',
'CUDA available': True,
'numpy_random_seed': 2147483648,
'GPU 0': 'NVIDIA GeForce RTX 4090',
'CUDA_HOME': '/usr/local/cuda-11.1',
'NVCC': 'Cuda compilation tools, release 11.1, V11.1.105',
'GCC': 'gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0',
'PyTorch': '1.10.0',
'TorchVision': '0.11.1',
'OpenCV': '4.7.0',
'MMEngine': '0.7.0',
'MMCV': '2.0.1',
'MMPreTrain': '1.0.0rc7+'}
其他信息
No response
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