Nanase1234/scannet-intention-10k
ScanNet Intention Dataset Indoor scene images with human intention annotations for object interaction. Dataset Description This dataset is derived from ScanNet indoor scene scans, with GPT-generated intention annotations. Each sample contains an indoor scene image, a target object, and 3 plausible human intentions. Splits Split Scenes Images/Queries train 596 8,853 test 100 1,855 Note: Train and test splits are separated by… See the full description on the dataset page: https://huggingface.co/datasets/Nanase1234/scannet-intention-10k.
ScanNet Intention Dataset
Indoor scene images with human intention annotations for object interaction.
Dataset Description
This dataset is derived from ScanNet indoor scene scans, with GPT-generated intention annotations. Each sample contains an indoor scene image, a target object, and 3 plausible human intentions.
Splits
Note: Train and test splits are separated by physical scene to prevent data leakage.
Fields
Quick Start
from datasets import load_dataset
ds = load_dataset("Nanase1234/scannet-intention-10k")
# Access train split
train_sample = ds["train"][0]
print(train_sample["target_category"])
print(train_sample["intention_1"])
# Access test split
test_sample = ds["test"][0]
print(test_sample["scene"])Statistics
- Total Samples: 10,708
- Physical Scenes: 696
- Object Categories: 34
- Train/Test Split: Scene-level (no scene overlap)
BBox Format
COCO format: [x, y, width, height]
x, y: Top-left corner coordinates (pixels)width, height: Box dimensions (pixels)
