SunDs is a collection of ready-to-use datasets for scene understanding tasks (3d object detection, semantic segmentation, nerf rendering,...). It provides:
- An API to easily load datasets to feed into your ML models.
- A collection of ready-to-use datasets.
- Helper tools to create new datasets.
import sunds
ds = sunds.load('kubric:nerf_synthetic/lego', split='train', task=sunds.tasks.Nerf())
for ex in ds:
ex['ray_origin']To use sunds, see the documentation:
Some datasets are pre-processed and published directly in
gs://kubric-public/tfds. You can stream them directly from GCS with:
sunds.load('kubric:nerf_synthetic/lego')The kubric: prefix is just an alias for
sunds.load('nerf_synthetic/lego', data_dir='gs://kubric-public/tfds')For best performance, it's recommended to copy the data locally with gsutil:
pip install gsutil # Only once
# Download the `nerf_synthetic_frames` and `nerf_synthetic_scenes` datasets
DATA_DIR=~/tensorflow_datasets/
mkdir $DATA_DIR
gsutil -m cp -r gs://kubric-public/tfds/nerf_synthetic_*/ $DATA_DIRAfter the data has been copied locally, it can be loaded directly.
sunds.load('nerf_synthetic/lego')If you copy locally to another folder than ~/tensorflow_datasets/,
you'll have to specify data_dir='/path/to/tfds/'.
This is not an official Google product.
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