Datasets:
pretty_name: PRICE
license: apache-2.0
language:
- en
size_categories:
- n<1K
task_categories:
- image-to-image
tags:
- image-generation
- image-to-image
- robotics
- embodied-ai
- benchmark
configs:
- config_name: default
drop_labels: true
PRICE: Prediction of Real-world Interactions with Constraints Evaluation
PRICE-V0.1 is a benchmark for instruction-conditional image-to-image generation grounded in real-world robot, egocentric, and third-person interaction data.
Given an initial-state image and a natural-language instruction, a model predicts the target-state image resulting from the instructed action. The benchmark evaluates instruction following, scene consistency, and physical plausibility.
The evaluation code and protocol live in evaluation/image_gen/PRICE/ in orca-wm/Orca.
Benchmark overview
Each PRICE example pairs a natural-language instruction and an initial-state image with a reference target-state image. The examples below illustrate the benchmark's diverse robot and egocentric interaction scenes, viewpoints, and action outcomes.
Examples from PRICE. Each pair shows the initial state and the target state corresponding to the instruction.
Dataset structure
The dataset contains one test split with 100 samples and the following principal columns:
| Column | Type | Description |
|---|---|---|
id | string | Stable PRICE sample identifier. |
query | image | Initial-state image. |
output | image | Reference target-state image. |
lang | string | Natural-language action instruction. |
dataset | string | Source collection identifier. |
| Source | Samples |
|---|---|
agibot_world | 30 |
homeinteract | 20 |
pe_video | 20 |
psi_ego | 30 |
| Total | 100 |
Additional provenance fields, timestamps, indices, random seed, and image checksums are retained from the source manifest.
Usage
Load PRICE directly from Hugging Face:
from datasets import load_dataset
dataset = load_dataset("BAAI/PRICE", split="test")
sample = dataset[0]
initial_image = sample["query"]
target_image = sample["output"]
instruction = sample["lang"]
Load the prepared folder locally:
from datasets import load_dataset
dataset = load_dataset("imagefolder", data_dir="data", split="test")
Evaluation
Use the evaluator and judge prompts from the GitHub repository. Model predictions should be named by the corresponding id, following the layout documented there.
Data sources
PRICE-V0.1 contains selected samples derived from AgiBot World, HomeInteract, PE-Video, and PsiBot SynData. Consult the PRICE paper and GitHub repository for complete source citations and selection details.
License
PRICE is released under the Apache License 2.0.
Version
This layout corresponds to PRICE-V0.1. Tag the Hugging Face Dataset repository with v0.1 and pin that revision from the evaluation code for reproducible results.