| --- |
| license: cc-by-4.0 |
| task_categories: |
| - visual-question-answering |
| language: |
| - en |
| pretty_name: BedBench |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: T1_uncolor |
| data_files: |
| - split: train |
| path: data/T1_uncolor/train-*.parquet |
| - config_name: T1_uncolor_axes |
| data_files: |
| - split: train |
| path: data/T1_uncolor_axes/train-*.parquet |
| - config_name: T1_color |
| data_files: |
| - split: train |
| path: data/T1_color/train-*.parquet |
| - config_name: T2_uncolor |
| data_files: |
| - split: train |
| path: data/T2_uncolor/train-*.parquet |
| - config_name: T2_uncolor_axes |
| data_files: |
| - split: train |
| path: data/T2_uncolor_axes/train-*.parquet |
| - config_name: T3_uncolor_axes |
| data_files: |
| - split: train |
| path: data/T3_uncolor_axes/train-*.parquet |
| - config_name: T3_color_axes |
| data_files: |
| - split: train |
| path: data/T3_color_axes/train-*.parquet |
| - config_name: T4_color |
| data_files: |
| - split: train |
| path: data/T4_color/train-*.parquet |
| - config_name: T4_color_axes |
| data_files: |
| - split: train |
| path: data/T4_color_axes/train-*.parquet |
| - config_name: T5_color |
| data_files: |
| - split: train |
| path: data/T5_color/train-*.parquet |
| - config_name: T5_color_axes |
| data_files: |
| - split: train |
| path: data/T5_color_axes/train-*.parquet |
| - config_name: T5_finetune |
| data_files: |
| - split: train |
| path: data/T5_finetune/train-*.parquet |
| - split: validation |
| path: data/T5_finetune/validation-*.parquet |
| - config_name: T6_uncolor |
| data_files: |
| - split: train |
| path: data/T6_uncolor/train-*.parquet |
| - config_name: T6_uncolor_axes |
| data_files: |
| - split: train |
| path: data/T6_uncolor_axes/train-*.parquet |
| - config_name: T7_color |
| data_files: |
| - split: train |
| path: data/T7_color/train-*.parquet |
| - config_name: T7_color_axes |
| data_files: |
| - split: train |
| path: data/T7_color_axes/train-*.parquet |
| - config_name: T8_uncolor |
| data_files: |
| - split: train |
| path: data/T8_uncolor/train-*.parquet |
| - config_name: T8_uncolor_axes |
| data_files: |
| - split: train |
| path: data/T8_uncolor_axes/train-*.parquet |
| - config_name: T9_uncolor |
| data_files: |
| - split: train |
| path: data/T9_uncolor/train-*.parquet |
| - config_name: T9_uncolor_axes |
| data_files: |
| - split: train |
| path: data/T9_uncolor_axes/train-*.parquet |
| - config_name: T10_color |
| data_files: |
| - split: train |
| path: data/T10_color/train-*.parquet |
| - config_name: T10_color_axes |
| data_files: |
| - split: train |
| path: data/T10_color_axes/train-*.parquet |
| - config_name: T11_invalid_uncolor |
| data_files: |
| - split: train |
| path: data/T11_invalid_uncolor/train-*.parquet |
| - config_name: T11_invalid_uncolor_axes |
| data_files: |
| - split: train |
| path: data/T11_invalid_uncolor_axes/train-*.parquet |
| - config_name: T11_invalid_color |
| data_files: |
| - split: train |
| path: data/T11_invalid_color/train-*.parquet |
| - config_name: T11_valid_uncolor |
| data_files: |
| - split: train |
| path: data/T11_valid_uncolor/train-*.parquet |
| - config_name: T11_valid_uncolor_axes |
| data_files: |
| - split: train |
| path: data/T11_valid_uncolor_axes/train-*.parquet |
| - config_name: T11_valid_color |
| data_files: |
| - split: train |
| path: data/T11_valid_color/train-*.parquet |
| - config_name: T12_uncolor |
| data_files: |
| - split: train |
| path: data/T12_uncolor/train-*.parquet |
| - config_name: T12_uncolor_axes |
| data_files: |
| - split: train |
| path: data/T12_uncolor_axes/train-*.parquet |
| - config_name: T12_color |
| data_files: |
| - split: train |
| path: data/T12_color/train-*.parquet |
| - config_name: T13_invalid_uncolor |
| data_files: |
| - split: train |
| path: data/T13_invalid_uncolor/train-*.parquet |
| - config_name: T13_invalid_color |
| data_files: |
| - split: train |
| path: data/T13_invalid_color/train-*.parquet |
| - config_name: T13_valid_uncolor |
| data_files: |
| - split: train |
| path: data/T13_valid_uncolor/train-*.parquet |
| - config_name: T13_valid_color |
| data_files: |
| - split: train |
| path: data/T13_valid_color/train-*.parquet |
| --- |
| # BedBench |
|
|
| Evaluation data, model outputs, and probing features for the paper |
| *Where Do Vision-Language Models Fail at 3D Reasoning?* Every task is posed on rendered views of 100 procedurally generated voxel objects (T1-T12) or 100 |
| multi-object scenes (T13). Code (generator, evaluation harness, probing, fine-tuning) is released separately. |
|
|
| ## Configurations |
|
|
| Each configuration is one task under one visual condition (the default setting is listed first). |
|
|
| | Task | Configurations | |
| | ---- | ---------------------------------------------------------------------------------------------------------------------------- | |
| | T1 | `uncolor`, `uncolor_axes`, `color` | |
| | T2 | `uncolor`, `uncolor_axes` | |
| | T3 | `uncolor_axes`, `color_axes` | |
| | T4 | `color`, `color_axes` | |
| | T5 | `color`, `color_axes` | |
| | T6 | `uncolor`, `uncolor_axes` | |
| | T7 | `color`, `color_axes` | |
| | T8 | `uncolor`, `uncolor_axes` | |
| | T9 | `uncolor`, `uncolor_axes` | |
| | T10 | `color`, `color_axes` | |
| | T11 | `invalid_uncolor`, `invalid_uncolor_axes`, `invalid_color`, `valid_uncolor`, `valid_uncolor_axes`, `valid_color` | |
| | T12 | `uncolor`, `uncolor_axes`, `color` | |
| | T13 | `invalid_uncolor`, `invalid_color`, `valid_uncolor`, `valid_color` | |
|
|
| `T5_finetune` holds the 800 training and 100 validation objects of the T5 fine-tuning experiment (generator seed |
| 100; no object overlaps the benchmark). |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("wnkh/BedBench", "T5_color", split="train") |
| ``` |
|
|
| The evaluation harness reads this repository directly: `python -m bed_eval run --dataset wnkh/BedBench ...`. |
|
|
| ## Extras |
|
|
| | File | Contents | |
| | ---------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | |
| | `extras/samples.tar.gz` | the 100 objects (manifest with all targets, every rendered view, projections, distractors) and the 100 T13 scenes, as written by the generator | |
| | `extras/outputs.tar.gz` | every model response used in the paper (12 open-weight models x 2 modes x 5 runs, 4 frontier models, image-removal, answer-format, reasoning-budget, factorized-T5, and fine-tuned runs) with ground truth;`bed_eval score outputs` reproduces the paper's numbers | |
| | `extras/probing_features.tar` | mean-pooled features of the 14 frozen encoders used in the probing analysis | |
|
|
| ## Developer |
|
|
| Main developer: [Nguyen Kim Hai Bui](https://whynotkimhari.github.io) ([GitHub](https://github.com/whynotkimhari)). |
|
|
| ## License |
|
|
| CC BY 4.0. |
|
|
