One unified interface for 5 UOIS datasets.
Load, augment, train, and evaluate — in 3 lines of code.

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Why uois_toolkit?

Problem	Solution
Every UOIS dataset ships its own format and loader	Unified API — one interface across all 5 datasets
Evaluation setup eats up research time	Built-in metrics — F1, IoU, Precision, Recall in a single call
Mixing synthetic and real data takes custom wiring	Multi-dataset DataModule with balanced sampling out of the box
Reproducing baselines means writing glue code	Lightning-native — drop into any training loop

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Quickstart

pip install uois-toolkit

from uois_toolkit import get_datamodule, cfg

dm = get_datamodule("ocid", "/path/to/OCID", batch_size=4, config=cfg)
dm.setup()
batch = next(iter(dm.train_dataloader()))
# batch["image_color"]  → [B, 3, H, W]
# batch["depth"]        → [B, C, H, W]
# batch["annotations"]  → per-image bboxes + RLE masks

Same API for all datasets — just swap the name: "tabletop", "osd", "robot_pushing", "iteach_humanplay".

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Supported Datasets

Dataset	Type	Images	Setting	Source
Tabletop (TOD)	Synthetic	~280K	Rendered household scenes	Xiang et al., CoRL 2020
OCID	Real	2,390	Cluttered tabletop	Suchi et al., ICRA 2019
OSD	Real	111	Sparse tabletop	Richtsfeld et al., IROS 2012
Robot Pushing	Real	428	Robot pushing objects	Lu et al., RSS 2023
iTeach-HumanPlay	Real	14K+	Human-object interaction	P et al., arXiv 2024

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Usage

Load a single dataset

from uois_toolkit.datasets import OCIDDataset
from uois_toolkit import cfg

dataset = OCIDDataset(image_set="test", data_path="/path/to/OCID", config=cfg)
sample = dataset[0]
# Keys: file_name, image_id, height, width, image_color, depth, raw_depth, annotations

Evaluate predictions

import numpy as np
from uois_toolkit.metrics import compute_metrics

gt_mask = ...   # [H, W] binary
pred_mask = ... # [H, W] binary

results = compute_metrics(gt_mask, pred_mask, ["f1_score", "iou", "precision", "recall"])
# {'f1_score': 0.89, 'iou': 0.80, 'precision': 0.92, 'recall': 0.86}

Train with PyTorch Lightning

from uois_toolkit import get_datamodule, cfg

dm = get_datamodule("tabletop", "/data/tabletop", batch_size=8, config=cfg)
trainer = pl.Trainer(accelerator="auto", max_epochs=10)
trainer.fit(model, datamodule=dm)

Batch format

Key	Shape	Description
image_color	[B, 3, H, W]	RGB image (float)
depth	[B, C, H, W]	Depth map
annotations	List[List[Dict]]	Per-image object annotations

Each annotation: {"bbox": [x1,y1,x2,y2], "segmentation": <RLE>, "category_id": 1}

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Installation from source

git clone https://github.com/jishnujayakumar/uois_toolkit.git
cd uois_toolkit
pip install -e .

Note: detectron2 is needed for mask utilities. Install a build that matches your PyTorch + CUDA version.

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Testing

python -m pytest test/test_datamodule.py -v \
  --dataset_path tabletop=/data/tabletop \
  --dataset_path ocid=/data/OCID \
  --dataset_path osd=/data/OSD

CI runs on every push and PR via GitHub Actions.

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Citation

@software{uois_toolkit,
  author = {Jishnu Jaykumar P and Aggarwal, Avaya and Maheshwari, Animesh},
  title = {uois_toolkit: A PyTorch Toolkit for Unseen Object Instance Segmentation},
  year = {2025},
  url = {https://github.com/jishnujayakumar/uois_toolkit}
}

Dataset citations (click to expand)

Tabletop (TOD) — Xiang et al., "Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation", CoRL 2020

OCID — Suchi et al., "EasyLabel: A Semi-Automatic Pixel-wise Object Annotation Tool for Creating Robotic RGB-D Datasets", ICRA 2019

OSD — Richtsfeld et al., "Segmentation of Unknown Objects in Indoor Environments", IROS 2012

Robot Pushing — Lu et al., "Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction", RSS 2023

iTeach-HumanPlay — P et al., "iTeach: In the Wild Interactive Teaching for Failure-Driven Adaptation of Robot Perception", arXiv:2410.09072

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Contributors

Built and polished by:

 

@OnePunchMonk • @AnimeshMaheshwari22

PRs welcome! See open issues.

License

MIT