Classification Training = Caffe+CaffeNet+ImageNet

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1. train, test 폴더 생성 및 이미지 추가
  • 이미지에 일련번호, JPEG
  • data/ilsvrc12/ILSVRC2012_img_train/
  • data/ilsvrc12/ILSVRC2012_img_val/ 폴더로 복사.
2. train.txt, val.txt 파일 작성
- data/ilsvrc12/ 에 작성.

 3. Leveldb (.lmdb) 파일 생성
EXAMPLE=examples/imagenet
DATA=data/ilsvrc12
TOOLS=build/tools

TRAIN_DATA_ROOT=data/ilsvrc12/ILSVRC2012_img_train/
VAL_DATA_ROOT=data/ilsvrc12/ILSVRC2012_img_val/
  • $ ./examples/imagenet/create_imagenet.sh 실행.
4. 평균이미지 생성
  • $ ./examples/imagenet/make_imagenet_mean.sh
5. Solver 편집
  • ./models/bvlc_reference_caffenet/solver.prototxt
  • solver_mode: CPU 로 변경
6. train 실행
  • $ ./build/tools/caffe train --solver=models/bvlc_reference_caffenet/solver.prototxt

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