---
license: cc-by-4.0
pretty_name: COSPLAN
configs:
- config_name: Maze-E
  data_files:
  - split: test
    path: checkboard/metadata.jsonl
- config_name: Blocksworld-E
  data_files:
  - split: test
    path: blocksworld/metadata.jsonl
- config_name: Shuffle-E
  data_files:
  - split: test
    path: imagenet/metadata.jsonl
- config_name: Robovqa-E
  data_files:
  - split: test
    path: robovqa/metadata.jsonl
---

# COSPLAN

A composite visual-planning benchmark with four multiple-choice subsets. Each
subset is a separate config in HuggingFace `imagefolder` format (images +
`metadata.jsonl`). Every row has an `image`, a `prompt`, and a `correct_option`
(A/B/C/D).

All four subsets are **step-completion** tasks: given a partial sequence (with one
erroneous step) and an image, select the option that correctly completes the task.

| Config | Items | Task |
|--------|-------|------|
| `Maze-E`         | 5000 | Complete the path from start to goal around obstacles |
| `Blocksworld-E`  | 5000 | Complete the block-move sequence to the target configuration |
| `Shuffle-E`      | 5000 | Complete the patch-swap sequence to rearrange into the target image |
| `Robovqa-E`      |  390 | Complete the robot action sequence |

## Usage

```python
from datasets import load_dataset

ds = load_dataset("shrg7/COSPLAN", "Blocksworld-E", split="test")

row = ds[0]
print(row["prompt"], row["correct_option"])

# The image is decoded as a PIL.Image under the "image" column
img = row["image"]
print(img.size, img.mode)
img.save("example.png")   # or img.show()
```