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---
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](https://omniobject3d.github.io/)
- ycbv (50k images): Contains the [YCB Video objects](https://rse-lab.cs.washington.edu/projects/posecnn/)
- tless (50k images): Contains the [TLESS objects](http://cmp.felk.cvut.cz/t-less/)
- 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
<!-- Provide the basic links for the dataset. -->
- **Repository:** TBA
- **Paper:** TBA
## Dataset Structure
TBA
## Citation
**BibTeX:**
TBA
## Dataset Card Authors and Contact
Lukas Dirnberger ([email protected]) |