metadata

configs:
  - config_name: omni
    data_files:
      - path: data/train/omni/*.tar
        split: train
  - config_name: cps
    data_files:
      - path: data/train/cps/*.tar
        split: train
  - config_name: ycbv
    data_files:
      - path: data/train/ycbv/*.tar
        split: train
  - config_name: tless
    data_files:
      - path: data/train/tless/*.tar
        split: train
task_categories:
  - zero-shot-classification
  - object-detection
  - depth-estimation
  - image-classification
  - image-segmentation
  - image-feature-extraction
  - image-to-3d
  - zero-shot-object-detection
pretty_name: Dropjects
size_categories:
  - 10K<n<1M

Dataset Card for Dropjects

Dropjects is a synthetic stereo RGB-D object dataset, created at the Chair of Cyber-Physical Systems in Production Engineering at the Technical University of Munich. It contains pose, bounding box, and segmentation masks for different sets of objects.

Dataset Details

Subsets

You can choose subsets with different sets of objects. Currently, there are the following subsets/object sets:

omni (500k images): Contains ~6k objects of the OmniObject3D dataset
ycbv (50k images): Contains the YCB Video objects
tless (50k images): Contains the TLESS objects
cps (50k images): Contains the Dropjects objects (TBA)

Then you can load the dataset like this, for example all lighting conditions for the stapler in the box, with clutter

from datasets import load_dataset

ds = load_dataset("LukasDb/dropjects", "omni", streaming=True, trust_remote_code=True, split='train')
for data in ds.with_format('tensorflow'):
    rgb = data['rgb'] # tf.uint8 Tensor, (h,w,3)

Dataset Description

Curated by: [email protected]
License: CC

Dataset Sources

Repository: TBA
Paper: TBA

Dataset Structure

TBA

Citation

BibTeX: TBA

Dataset Card Authors and Contact

Lukas Dirnberger ([email protected])