Open polooloo opened 4 years ago
I tested it according to your directory structure and found that I can't train the network. Your train.prototxt should contain at least the input and output data sets that need to be trained, right?
I tested it according to your directory structure and found that I can't train the network. Your train.prototxt should contain at least the input and output data sets that need to be trained, right?
训练文件不全,loss函数领有文件,这几天在准备毕业论文的事,我会全部公开,包括caffe怎样用Python写自己的loss函数等一些经验教程
Could you please provide me with a full training profile?
I tested it according to your directory structure and found that I can't train the network. Your train.prototxt should contain at least the input and output data sets that need to be trained, right?
训练文件不全,loss函数领有文件,这几天在准备毕业论文的事,我会全部公开,包括caffe怎样用Python写自己的loss函数等一些经验教程
Could you please provide me with a full training profile?
I tested it according to your directory structure and found that I can't train the network. Your train.prototxt should contain at least the input and output data sets that need to be trained, right?
训练文件不全,loss函数领有文件,这几天在准备毕业论文的事,我会全部公开,包括caffe怎样用Python写自己的loss函数等一些经验教程
Could you please provide me with a full training profile?
In a few days
I tested it according to your directory structure and found that I can't train the network. Your train.prototxt should contain at least the input and output data sets that need to be trained, right?
训练文件不全,loss函数领有文件,这几天在准备毕业论文的事,我会全部公开,包括caffe怎样用Python写自己的loss函数等一些经验教程
Could you please provide me with a full training profile?
In a few days
Looking forward to the guidance of the Great God!
This mbv3 / train.prototxt cannot be used. It should be in this format under normal circumstances: Should include TRAIN and TEST parsing parameters. Please correct it as soon as possible.
layer { name: "data" type: "Data" top: "data" top: "label" include { phase: TRAIN } transform_param { mean_value: 132 mean_value: 128 mean_value: 128 mirror: true } data_param { source: "D: / BLTDeep / MobileNetV3 / caffe-windows / models / EfficientNet-Caffe / lmdb / train" batch_size: 8 backend: LMDB } } layer { name: "data" type: "Data" top: "data" top: "label" include { phase: TEST }
mean pixel / channel-wise mean instead of mean image
transform_param { mean_value: 132 mean_value: 128 mean_value: 128 mirror: false } data_param { source: "D: / BLTDeep / MobileNetV3 / caffe-windows / models / EfficientNet-Caffe / lmdb / test" batch_size: 8 backend: LMDB } }