Deepdrive Dataset to TFRecord Convert the Berkeley Deepdrive dataset to a TFRecord file. (Specifically for the Object Detection Task)
This repository shall help to create a tfrecord file for the berkeley
deep drive dataset. I have no affiliation with Berkeley and/or the deep
drive team.
Now also supports the new data format
Download dataset Register at http://bdd-data.berkeley.edu/login.html . NOTE: The server does not serve an SSL ceritificate.
Go to the Download page http://bdd-data.berkeley.edu/portal.html#download
Accept the License and Download: "Images", "Labels"
Create a folder ~/.deepdrive/download
Place both zip files in that folder
Create dataset You can use the script create_tfrecord.py in order to create the TFRecord file you need.
--fold_type = ['train', 'val', 'test'] : Select for which fold you want to create the tfrecord (default=train)
--version = ['100k', '10k'] : The Berkeley Deepdrive Dataset comes in two sizes. (default=100k)
--elements_per_tfrecord = integer : You can specify, how many images
are put into one tfrecord file. Multiple TFRecord files are generated.
--number_images_to_write = integer : Restricts the number of files to
be written. [E.g. to create smaller files to test overfitting]
--weather = str : Specify the weather which should be written to the tfrecord
--scene_type = str : Specify the scene_type which should be written to the tfrecord
--daytime = str : Restrict the daytime which should be written to the tfrecord
The resulting TFRecord files can be found in :
~/.deepdrive/tfrecord/[version]/[fold_type]/
Read dataset Using read_data.py you can check your TFRecord file.
--batch_size = int: Specify the batch-size
--fold_type = see above
--version = see above
It will plot all images, and all boundingboxes.