caffe训练自己的数据集

利用python生成txt文件后，就可以利用caffe自带工具将图片转换为lmdb格式，其代码如下

#!/usr/bin/env sh# Create the imagenet lmdb inputs# N.B. set the path to the imagenet train + val data dirsset -eEXAMPLE=/home/yh/caffe/examples/H-WDATA=/home/yh/caffe/examples/H-WTOOLS=/home/yh/caffe/build/toolsTRAIN_DATA_ROOT=/home/yh/caffe/examples/H-W/train/VAL_DATA_ROOT=/home/yh/caffe/examples/H-W/val/# Set RESIZE=true to resize the images to 256x256. Leave as false if images have# already been resized using another tool.RESIZE=trueif $RESIZE; then  RESIZE_HEIGHT=256  RESIZE_WIDTH=256else  RESIZE_HEIGHT=0  RESIZE_WIDTH=0fiif [ ! -d "$TRAIN_DATA_ROOT" ]; then  echo "Error: TRAIN_DATA_ROOT is not a path to a directory: $TRAIN_DATA_ROOT"  echo "Set the TRAIN_DATA_ROOT variable in create_imagenet.sh to the path" \       "where the ImageNet training data is stored."  exit 1fiif [ ! -d "$VAL_DATA_ROOT" ]; then  echo "Error: VAL_DATA_ROOT is not a path to a directory: $VAL_DATA_ROOT"  echo "Set the VAL_DATA_ROOT variable in create_imagenet.sh to the path" \       "where the ImageNet validation data is stored."  exit 1fiecho "Creating train lmdb..."GLOG_logtostderr=1 $TOOLS/convert_imageset \    --resize_height=$RESIZE_HEIGHT \    --resize_width=$RESIZE_WIDTH \    --shuffle \    $TRAIN_DATA_ROOT \    $DATA/trainlist.txt \    $EXAMPLE/hw_train_lmdbecho "Creating val lmdb..."GLOG_logtostderr=1 $TOOLS/convert_imageset \    --resize_height=$RESIZE_HEIGHT \    --resize_width=$RESIZE_WIDTH \    --shuffle \    $VAL_DATA_ROOT \    $DATA/val.txt \    $EXAMPLE/hw_val_lmdbecho "Done."