File size: 3,077 Bytes
a8625b9 93bd88d e10bdca a8625b9 711b2ce a8625b9 89568d4 a8625b9 89568d4 a8625b9 24cfcf0 a8625b9 24cfcf0 a8625b9 89568d4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | ---
license: cc-by-4.0
---
# Dataset Description
**RoboAfford** is a large-scale dataset with dense, affordance-aware annotations for instruction grounded manipulation.
This dataset contains **819,987 images** and **1.9 million QA pairs**, unifying object affordances and spatial affordances to support interaction-centric learning in robotics.
# Dataset Composition
**RoboAfford** aggregates images from multiple datasets and generates QA pairs to provide a comprehensive dataset for affordance understanding.
It consists of the following components:
- **LVIS_absxy_513K.json**: 513K object detection QA pairs for 152,152 images sourced from [LVIS](https://www.lvisdataset.org/)
- **pointing_absxy_190K.json**: 190K object pointing QA pairs for 63,907 images selected from [PixMo-Points](https://huggingface.co/datasets/allenai/pixmo-points)
- **object_affordance_prediction_absxy_561K.json**: 561K object affordance prediction QA pairs for 45,790 images sourced from [PACO-LVIS](https://github.com/facebookresearch/paco)
- **object_ref_max_points_10_absxy_347K.json**: 347K object reference QA pairs for 287,956 images sourced from [RoboPoint-data](https://huggingface.co/datasets/wentao-yuan/robopoint-data)
- **region_ref_max_points_10_absxy_320K.json**: 320K region reference QA pairs for 270,182 images sourced from [RoboPoint-data](https://huggingface.co/datasets/wentao-yuan/robopoint-data)
# Dataset Format
Each json file contains a list of structured conversations with image references. An example QA pair and its associated image are shown as follows:
```
{
"id": "paco_403013",
"image": "train2017/000000403013.jpg",
"conversations": [
{
"from": "human",
"value": "<image>\nWhat appliance can be used to heat food quickly? Your answer should be formatted as a list of tuples, i.e. [(x1, y1, x2, y2), ...], where each tuple contains the x, y coordinates of the top-left corner and bottom-right corner of the bounding box. The coordinates should be rounded to two decimal places, indicating the absolute pixel locations of the points in the image."
},
{
"from": "gpt",
"value": "[(258.14, 174.87, 283.79, 222.17)]"
},
{
"from": "human",
"value": "What appliance can be used to heat food quickly? Your answer should be formatted as a list of tuples, i.e. [(x1, y1), (x2, y2), ...], where each tuple contains the x and y coordinates of a point on the object. The coordinates should be rounded to two decimal places, indicating the absolute pixel locations of the points in the image."
},
{
"from": "gpt",
"value": "[(258.61, 213.0)]"
}
]
}
```
# Evaluation
For benchmarking protocols and evaluation metrics, please refer to [RoboAfford-Eval](https://huggingface.co/datasets/tyb197/RoboAfford-Eval) and [https://github.com/tyb197/RoboAfford](https://github.com/tyb197/RoboAfford) for more detailed information. |