python - tensorflow object detection Fine-tuning a model from an existing checkpoint -


i'm trying train model existing checkpoint following these instructions.

i have configured object detection training pipeline using faster_rcnn_resnet101_voc07.config configuration.

in checkpoint section have set directory located checkpoint files of pretrained model faster_rcnn_resnet101_coco.tar.gz

acording issue fine_tune_checkpoint can path directory containing 3 files: (.data-00000-of-00001, .index, .meta).

so set path directory "/home/docs/car_dataset/models/model/train"

gradient_clipping_by_norm: 10.0   fine_tune_checkpoint: "/home/docs/car_dataset/models/model/train"   from_detection_checkpoint: true   num_steps: 800000   data_augmentation_options {     random_horizontal_flip {     }   }

however when execute script training:

python object_detection/train.py     --logtostderr\ --pipeline_config_path=/home/docs/car_dataset/models/model/faster_rcnn_resnet101_voc07.config\ --train_dir=/home/docs/car_dataset/models/model/train\ --num_gpus=2

i got error:

tensorflow.python.framework.errors_impl.datalosserror: unable open table file /home/docs/car_dataset/models/model/train: failed precondition: /home/docs/car_dataset/models/model/train: perhaps file in different file format , need use different restore operator?

i have tried setting path every file in directory

fine_tune_checkpoint: "/home/docs/car_dataset/models/model/train/model.ckpt.meta"

but error:

tensorflow.python.framework.errors_impl.datalosserror: unable open table file /home/docs/car_dataset/models/model/train/model.ckpt.meta: data loss: not sstable (bad magic number): perhaps file in different file format , need use different restore operator?

what correct way define pre trained model in pipeline configuration having 3 files: (.data-00000-of-00001, .index, .meta).

tensorflow version: 1.2.1

what have specify whole path without ".meta", ".index" , ".data-00000-of-00001" extensions. in case, looks be: "/home/docs/car_dataset/models/model/train/model.ckpt" (which you'll notice more specific directory).


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