Update README.md

README.md

@@ -1,5 +1,4 @@
 ---
-license: cc
 configs:
 - config_name: 'omni'
   data_files:
@@ -17,4 +16,64 @@ configs:
   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='test')
+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])