Suggestions on the documentation in part generating traning data for ssd

Thanks a lot for the nice work, but there’s some information missing related to generating training data for ssd, hope followings will save time for others who are also interested in this work:

for generating training data for ssd, instead using “detection_utils/generate_sixd_train.py”, “generate_syn_det_train.py” should be used. And the commanline configuration looks as follows:

python generate_syn_det_train.py \
     --output_path=/path/to/training_data_ssd  \  <==output dir given by user
     --model=/path/to/model_dir/t-less/t-less_v2/models_cad/  \ <==model data dir
     --num=3000 \ <==how many data u want to generate
      --scale=1 \ <==scale factors, In case of a model in meters, use 1000
      --vocpath=/localhome/demo/autoencoder_6d_pose_estimation/backgrounimage/VOCdevkit/VOC2012/JPEGImages  \ <==background pictures location
      --model_type=cad  <==when with textures use 'reconst'

after running, xml annotations and training images will be generated nicely.

[MartinSmeyer]

Thank you! I should add some documentation to it.

Both scripts should work, “generate_syn_det_train.py” creates synthetic object views from the 3D models and “detection_utils/generate_sixd_train.py” uses a SIXD training set (e.g. from T-LESS) with single object views on black background. As stated in the paper, for T-LESS we use the primesense training set. Just change the paths inside detection_utils/generate_sixd_train.py to point to the T-LESS and VOC datasets and you should be fine.

[MartinSmeyer]

Additionally, so both datasets. Also, for training you might need early stopping to avoid overfitting to the synthetic data.