Question about dataset_convert tool


I have two dataset

  1. dataset for training (18000 image & labels)
  2. dataset for valid ( 3000 images & labels)

According to the DetectNet_v2 — Transfer Learning Toolkit 3.0 documentation doc

It appears to separate one dataset into training and valid.

kitti_config {
  root_directory_path: "/workspace/tlt-experiments/data/VOCtrainval_11-May-2012/VOCdevkit/VOC2012"
  image_dir_name: "JPEGImages_kitti/test"
  label_dir_name: "Annotations_kitti/test"
  image_extension: ".jpg"
  partition_mode: "random"
  num_partitions: 2
  val_split: 20
  num_shards: 10
image_directory_path: "/workspace/tlt-experiments/data/VOCtrainval_11-May-2012/VOCdevkit/VOC2012"

but i want to make training set using my ‘dataset for training’ and make valid set using my ‘dataset for valid’.

this is possible??

It is possible.
Please generate tfrecords separately for your training dataset and validation dataset.

Then in training spec as below, set the tfreocrds files of your validation dataset.

validation_data_source: {
tfrecords_path: " /tfrecords validation pattern>"
image_directory_path: " "

and comment out

#validation_fold: 0

More info, see DetectNet_v2 — Transfer Learning Toolkit 3.0 documentation

i successed to transfer learning based on peopleNet

thanks to your advise Morganh