python - Tensorflow Object Detection API multi-class error -


i creating 11 class object detector using faster-rcnn model set maximum size of 300x400 in image-resizer tag. due cuda oom error popping if go higher gpu 1050 ti, 4gb ver, have approximately 3800-3900 mb of model run-time training memory.

i have followed erishima's steps , mutated them pet's scripts , dati tran's generate tfrecord files.

the steps follows:

  1. create labels categories using labelimg.
  2. use name field in labelimg annotate class of image file.
  3. create csv file , extract filename, class, xmin, ymin, xmax, ymax xml file. (custom script)
  4. create train , test/eval csv main csv file.
  5. generate tfrecord files inputted config file. train , test.(dati tran's script modified suit needs)
  6. modify faster_rcnn_config without touching hyper-parameters.
  7. created label_map.pbtxt file corresponded names of classes. started 1 stated in many other answers related topic.
  8. started training model via stated method.

the dataset classes custom , images/class varies 2500 300. dataset has no definition of orientation of object , difficulty of detection in image though every possible angle of object present in images.

the problem arises when have trained loss value of .002 after 217k steps single class enveloping objects of other classes whether ran detector on video or images. have not tried run eval.py script takes long on setup , can't see map classes assume should redundant information problem should in dataset set preparation method or in dataset itself.

when retrained anew 60k steps, problem persisted class enveloping other.

the warnings shown were:

  • the sparse index tensor going take alot of memory. can change code not pop-up , possibly save me precious memory.
  • wanted [x,?,?,y], got [x,y,z,a,b] instead. 1 stops training. got 2 times in training upto 217k steps. have no idea 1 originates; probably, dataset.

if can show me hint proper fix this, highly appreciate it.


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