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Browse files- README.md +272 -0
- meta/train/chunk_00000.parquet +3 -0
- meta/validation/chunk_00000.parquet +3 -0
- parquet_info.json +10 -0
- preview/train/chunk_00000.parquet +3 -0
- preview/validation/chunk_00000.parquet +3 -0
- reports/build/counters.json +88 -0
- reports/build/dropped.jsonl +158 -0
- reports/build/manifest.json +40 -0
- reports/build/quarantine.jsonl +0 -0
- reports/export_manifest.json +1767 -0
- reports/known_issues/summary.json +1 -0
- reports/s10_dropped.jsonl +0 -0
- reports/s10_label_conflicts.jsonl +0 -0
- reports/s13_dropped.jsonl +555 -0
- reports/s14_sample.tsv +201 -0
- train/chunk_00000.parquet +3 -0
- validation/chunk_00000.parquet +3 -0
README.md
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| 1 |
+
---
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| 2 |
+
license: cc-by-4.0
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| 3 |
+
pretty_name: "Spatial MMCoT v1 - vdrop"
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| 4 |
+
language:
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| 5 |
+
- en
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| 6 |
+
task_categories:
|
| 7 |
+
- visual-question-answering
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| 8 |
+
- image-to-image
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| 9 |
+
tags:
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| 10 |
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- spatial-reasoning
|
| 11 |
+
size_categories:
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| 12 |
+
- 1K<n<10K
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| 13 |
+
configs:
|
| 14 |
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- config_name: preview
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| 15 |
+
default: true
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| 16 |
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data_files:
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| 17 |
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- split: train
|
| 18 |
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path: preview/train/*.parquet
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| 19 |
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- split: validation
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| 20 |
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path: preview/validation/*.parquet
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| 21 |
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- config_name: train
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| 22 |
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data_files:
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| 23 |
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- split: train
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| 24 |
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path: train/*.parquet
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| 25 |
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- split: validation
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| 26 |
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path: validation/*.parquet
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| 27 |
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- config_name: meta
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| 28 |
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data_files:
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| 29 |
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- split: train
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| 30 |
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path: meta/train/*.parquet
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| 31 |
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- split: validation
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| 32 |
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path: meta/validation/*.parquet
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| 33 |
+
---
|
| 34 |
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|
| 35 |
+
# Spatial MMCoT v1 · `vdrop`
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| 36 |
+
|
| 37 |
+
VDrop cross-view (QianYangMILA/vdrop-crossview-8k, [arXiv:2605.27310](https://arxiv.org/abs/2605.27310)), Infinigen Indoors rooms. Two egocentric views of one room with partly overlapping fields of view are the input (`input_image_0` = cam0, `input_image_1` = cam1); the target is upstream's ground-truth `panorama` render, a wide panorama of the room that spans both views. The questions are multiple choice and the trained answer is the option letter. The upstream release ships no reasoning text at all, so every thought here is EMPTY: `output_text_list` is exactly `<think></think><image_start>` and `<image_end><think></think><answer>X</answer>`. The panorama belongs to the scene, not the question: every question about a scene shares that scene's two input views and its one panorama, so the 7,015 rows use 1,158 distinct panoramas. Report validation scores with confidence intervals clustered by scene (for example a bootstrap over `meta.scene_id`), not as independent rows. The scenes were generated with Infinigen Indoors (github.com/princeton-vl/infinigen, whose code is BSD-3-Clause); the renders are distributed by the VDrop authors under CC-BY-4.0. Changes from upstream: the PNG images are re-encoded as JPEG and downscaled (the two input views from 1280x720 to 512x288, the panorama to 1,024 px wide); of the upstream thinking images only the panorama is kept; perspective-taking questions phrased relative to 'your partner' are dropped (as the upstream authors' `build_interleaved_from_visual_only.py` does by default); rows are removed by answer-prior balancing and split into train/validation by scene (both below); the ThinkMorph system prompt is prepended to the unchanged question.
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| 38 |
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| 39 |
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**Supervision kind** (`supervision_kind` in `meta`): `visual_only` on every row: the thoughts are empty: the rows supervise the image and the answer only.
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| 40 |
+
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| 41 |
+
Upstream: [`QianYangMILA/vdrop-crossview-8k`](https://huggingface.co/datasets/QianYangMILA/vdrop-crossview-8k). Licence: **cc-by-4.0**.
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| 42 |
+
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| 43 |
+
## Known issues
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| 44 |
+
|
| 45 |
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No row of this source is listed in `reports/known_issues/` (its `summary.json` is empty); the measured caveats below still apply.
|
| 46 |
+
|
| 47 |
+
### Measured caveats
|
| 48 |
+
|
| 49 |
+
Measured on this release by the pre-publication review (2026-09-25): problems that cannot be listed row by row (a shortcut in the options, a label convention, an upstream labelling scheme) and what the review found around the lists above. Where a caveat counts listed rows ("listed as `...`"), the count is the table's, read from `reports/known_issues/summary.json`. Its other numbers are the review's own measurements, which no file carries: they hold for exactly these rows and are not re-measured automatically. Items marked **Training-signal defect** are problems in what the rows teach, not only in how they are described; no row was removed for them.
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| 50 |
+
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| 51 |
+
- Validation is strongly clustered by scene. Its 267 rows come from 43 scenes: one scene supplies 52 of them, and the three largest supply 113 (42.3%). In training, 1,115 scenes give a median of 3 rows per scene and at most 100 (one scene's 100 rows share one panorama). Report validation scores with confidence intervals clustered by scene, not as 267 independent rows.
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| 52 |
+
|
| 53 |
+
## Size
|
| 54 |
+
|
| 55 |
+
| split | rows | target image slots | distinct target images |
|
| 56 |
+
|---|---:|---:|---:|
|
| 57 |
+
| train | 6,748 | 6,748 | 1,115 |
|
| 58 |
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| validation | 267 | 267 | 43 |
|
| 59 |
+
|
| 60 |
+
A slot is one target position in one row; rows can share a target image (same scene or same intermediate state), and a row can repeat one of its own targets, so there are fewer distinct images (by content hash) than slots.
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| 61 |
+
|
| 62 |
+
| task | train | validation |
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| 63 |
+
|---|---:|---:|
|
| 64 |
+
| anchor | 690 | 21 |
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| 65 |
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| counting | 590 | 37 |
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| 66 |
+
| perspective_taking | 1,245 | 49 |
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| 67 |
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| relative_direction | 1,399 | 50 |
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| 68 |
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| relative_distance | 2,824 | 110 |
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| 69 |
+
|
| 70 |
+
Input images per row: 2. Target images per row (the images the model is trained to generate): 1.
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| 71 |
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Image corpus (`source_scene_corpus`): infinigen 7,015.
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| 72 |
+
|
| 73 |
+
## Row format
|
| 74 |
+
|
| 75 |
+
One row is: input image(s) and a question, then K rounds of *thought → target image* (the target is the source's own ground-truth image, which the model is trained to generate), then a final thought (normally a read-back of the last target; where a source's final thought is something else, or often leaves out the answer, the source note or Known issues says so) and the answer; here K is 1. In the `train` config:
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| 76 |
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| 77 |
+
```
|
| 78 |
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image_list list<binary> inputs first, then the K target images in order
|
| 79 |
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num_input_images int64 how many of image_list are inputs
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| 80 |
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instruction_list list<string> one element: system prompt + question + options
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| 81 |
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output_text_list list<string> K+1 elements:
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| 82 |
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[0] <think>plan 1</think><image_start>
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| 83 |
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[j] <image_end><think>plan j+1</think><image_start>
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| 84 |
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[K] <image_end><think>read-back</think><answer>answer</answer>
|
| 85 |
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row_uid string join key to `meta` and `preview`
|
| 86 |
+
```
|
| 87 |
+
|
| 88 |
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Every image is a JPEG, and no input image is larger than 512 px on its long edge (measured on this release, 2026-09-25); the size each target was stored at is `target_px` in `meta`.
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| 89 |
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| 90 |
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On every row `<answer>` holds the option key (answer type `mcq_letter` 7,015) that the model is trained to emit (a letter for `mcq_letter`), while `meta.answer_value` (the `answer` column of `preview`) holds that option's text. Map the key through the options listed in the question before comparing the two, and score model output against `<answer>`.
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| 91 |
+
|
| 92 |
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The system prompt is ThinkMorph's `VLM_THINK_SYSTEM_PROMPT` from its `inferencer.py`, verbatim (`GEN_THINK_SYSTEM_PROMPT` there has the same text), including its leading and trailing newline. The markers are plain strings, not tokenizer special tokens; the prompt writes `</image_end>` and the data writes `<image_end>`, exactly as the ThinkMorph-7B checkpoint was trained.
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| 93 |
+
|
| 94 |
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`preview` shows the same rows with one column per slot: `input_image_i` for the inputs; for each of the K = `num_steps` rounds, the plan `thought_j` and its target `target_image_j`; and the read-back in `thought_1` on every row.
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| 95 |
+
|
| 96 |
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`meta` holds the per-row sidecar: task, `scene_id` and `geometry_uid` (the scene and geometry keys; the split key is named in the split paragraph below), `trajectory_id` (a camera-path or sample label, empty where the source has none), `num_steps`, `num_input_images`, `answer_type`, `answer_value`, `majority_class_rate`, `target_image_kind`, `target_px`, `est_tokens`, licence, `split` (`train` / `validation`, the Hub split names), `supervision_kind` (`full_interleaved` / `visual_aux` / `visual_only`) and `filter_flags`. `majority_class_rate` is the share of the task's most frequent `answer_value` among its training rows: it measures answer skew and is not a guessing baseline (where a task mixes question types or each row has its own options it can be far below chance); compare scores with the text-only baselines below.
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| 97 |
+
|
| 98 |
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Per-row `license` in `meta`: cc-by-4.0 7,015.
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| 99 |
+
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| 100 |
+
Flags on released rows (`filter_flags` in `meta` and `preview`, comma-separated):
|
| 101 |
+
|
| 102 |
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| flag | rows | meaning |
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| 103 |
+
|---|---:|---|
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| 104 |
+
| `S5.replay_unsupported` | 7,015 | no solver re-derives this task's answer from the trace, so S5 did not replay it |
|
| 105 |
+
| `S14.sampled_qa` | 200 | chosen for the S14 human spot-check (`reports/s14_sample.tsv`) |
|
| 106 |
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|
| 107 |
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### Training with a BAGEL-family loader
|
| 108 |
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|
| 109 |
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Rows here have 2 input images: the first `num_input_images` entries of `image_list` are inputs and the rest are targets, so the loader must read `num_input_images`. The `UnifiedEditIterableDataset` of the IPT release (https://github.com/weikaih04/Imaginative-Perception-Token, `data/interleave_datasets/edit_dataset.py`) does: its `parse_row` conditions on `image_list[:num_input_images]` and trains the remaining images as targets, one after each `output_text_list` element but the last. The stock ThinkMorph loader (the same class in https://github.com/ThinkMorph/ThinkMorph) does not: it conditions on `image_list[0]` only and trains `image_list[j+1]` after `output_text_list[j]`, the answer element included, so every input after the first is trained as a generated image and every target moves one slot later per extra input. Every row here has one target, so on a row with exactly two inputs it trains `image_list[1]` (the second input view) after the plan and `image_list[2]` (the real target) after the answer. It raises no error. To use it, make two changes in its `parse_row`:
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| 110 |
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|
| 111 |
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```
|
| 112 |
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k = int(row.get("num_input_images", 1) or 1)
|
| 113 |
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for im in images[:k]: # replaces the single _add_image(images[0], ...)
|
| 114 |
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data = self._add_image(data, pil_img2rgb(Image.open(io.BytesIO(im))),
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| 115 |
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need_loss=False, need_vae=True, need_vit=True)
|
| 116 |
+
...
|
| 117 |
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img_idx = idx + k # replaces img_idx = idx + 1
|
| 118 |
+
```
|
| 119 |
+
|
| 120 |
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The stock BAGEL edit loader (ByteDance-Seed/Bagel) cannot train these rows: it never reads `output_text_list` and expects each `instruction_list` element to be a list of paraphrases.
|
| 121 |
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|
| 122 |
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`parquet_info.json` keys each training chunk as `<source>/<split>/<file>`, here `vdrop/train/chunk_00000.parquet`, with row-group counts read from the parquet footers. The loader matches a chunk only when its key equals the path it builds, `os.path.join(data_dir, file)`, and skips a chunk with no key without a warning: a source that is alone in its group then fails with `IndexError: list index out of range`, and in a mixed group it adds no rows. Download into a directory named after the source, not after the repository:
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| 123 |
+
|
| 124 |
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```python
|
| 125 |
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from huggingface_hub import snapshot_download
|
| 126 |
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snapshot_download("yrlyrl/spatial-mmcot-vdrop", repo_type="dataset", local_dir="<root>/vdrop",
|
| 127 |
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allow_patterns=["train/*", "validation/*", "parquet_info.json", "reports/known_issues/*"])
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| 128 |
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```
|
| 129 |
+
|
| 130 |
+
Then either run from `<root>` with `data_dir: vdrop/train` and `parquet_info_path: vdrop/parquet_info.json`, or rebuild the index with absolute keys and use an absolute `data_dir`:
|
| 131 |
+
|
| 132 |
+
```python
|
| 133 |
+
import json, os
|
| 134 |
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root = "/abs/path/to/root" # the directory that holds vdrop/
|
| 135 |
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info = json.load(open(os.path.join(root, "vdrop", "parquet_info.json")))
|
| 136 |
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info = {os.path.join(root, k): v for k, v in info.items()}
|
| 137 |
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json.dump(info, open(os.path.join(root, "vdrop", "parquet_info_abs.json"), "w"))
|
| 138 |
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# data_dir = os.path.join(root, "vdrop", "train") (spelled exactly so, no trailing slash)
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| 139 |
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# parquet_info_path = os.path.join(root, "vdrop", "parquet_info_abs.json")
|
| 140 |
+
```
|
| 141 |
+
|
| 142 |
+
The Hugging Face cache (`.../snapshots/<hash>/train/`) or a folder named `spatial-mmcot-vdrop` matches no key.
|
| 143 |
+
|
| 144 |
+
`num_used_data` counts chunk files, not rows: the loader repeats this source's file list up to that number, lists every (file, row group) pair, and deals whole row groups out, floor(R / world_size) to each rank and floor(that / num_workers) to each DataLoader worker. The remainder is never read. This source has 1 training chunk file holding 53 row groups of up to 128 rows, so keep `num_used_data` large, e.g. the 128 of ThinkMorph's `interleaved_reasoning.yaml` (upstream's `example.yaml` asks for more than GPUs x workers); every row group is then read. Set to 1 and alone in its group on 8 GPUs with 4 workers, it reads only 32 of the 53 row groups. In a run that mixes sources, give each source the same multiple of its own training chunk-file count, e.g. 128 per file (128 here): the file list is repeated up to `num_used_data` entries, so a flat 128 for every source would read a two-file source's rows half as often as a one-file source's.
|
| 145 |
+
|
| 146 |
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## How the rows were chosen
|
| 147 |
+
|
| 148 |
+
| stage | rows |
|
| 149 |
+
|---|---:|
|
| 150 |
+
| upstream rows read | 7,728 |
|
| 151 |
+
| refused before conversion (`S0raw`; each reason is in the table below) | 158 |
|
| 152 |
+
| after conversion and per-row filters | 7,570 |
|
| 153 |
+
| removed by S10 (none) | 0 |
|
| 154 |
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| removed by answer-prior balancing (S13) | 555 |
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| 155 |
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| **released** | **7,015** |
|
| 156 |
+
|
| 157 |
+
The rows refused before conversion are perspective-taking questions asked relative to "your partner's perspective": most name no image, so the partner's viewpoint cannot be recovered from the two input views, and the rest are dropped too, as the upstream authors' own build script does.
|
| 158 |
+
|
| 159 |
+
Every removed row has one line, with its reason, in `reports/`:
|
| 160 |
+
|
| 161 |
+
| file | step | reason (the line's `flag`, or the field shown) | rows |
|
| 162 |
+
|---|---|---|---:|
|
| 163 |
+
| `build/dropped.jsonl` | S0raw | `S3.partner_phrasing` | 158 |
|
| 164 |
+
| `s13_dropped.jsonl` | S13 | `step: rank` | 514 |
|
| 165 |
+
| `s13_dropped.jsonl` | S13 | `step: letter` | 41 |
|
| 166 |
+
|
| 167 |
+
Every line of `s13_dropped.jsonl` has `reason: prior_downsample`; `step` names the balancing pass that removed it, and `split` is written `train` or `val` (the Hub's `validation`).
|
| 168 |
+
|
| 169 |
+
`S0raw` lines in `build/dropped.jsonl` were refused before a release row existed, so their `row_uid` field holds the converter's key for the upstream record (the upstream `qa` config's `id`, e.g. `perspective_taking_000002`), not a 16-hex `row_uid`; lines from later steps carry the `row_uid` the row had. No removed row appears in `meta` or `preview`.
|
| 170 |
+
|
| 171 |
+
<details><summary>Per-step counters of the conversion</summary>
|
| 172 |
+
|
| 173 |
+
7,728 upstream rows were read; `S0raw` refused 158 before a row existed and passed 7,570 to the first step. `S0` runs once more, last, on the final bytes. The reason for every refused, dropped or quarantined row is in the files above.
|
| 174 |
+
|
| 175 |
+
| step | in | out | dropped | quarantined | rejected | repaired |
|
| 176 |
+
|---|---:|---:|---:|---:|---:|---:|
|
| 177 |
+
| S0raw (refused before conversion) | 7,728 | 7,570 | 0 | 0 | 158 | 0 |
|
| 178 |
+
| S4 | 7,570 | 7,570 | 0 | 0 | 0 | 0 |
|
| 179 |
+
| S4c | 7,570 | 7,570 | 0 | 0 | 0 | 0 |
|
| 180 |
+
| S5 | 7,570 | 7,570 | 0 | 0 | 0 | 0 |
|
| 181 |
+
| S8 | 7,570 | 7,570 | 0 | 0 | 0 | 0 |
|
| 182 |
+
| S9 | 7,570 | 7,570 | 0 | 0 | 0 | 0 |
|
| 183 |
+
| S0 (final structural check, after S9) | 7,570 | 7,570 | 0 | 0 | 0 | 0 |
|
| 184 |
+
|
| 185 |
+
</details>
|
| 186 |
+
|
| 187 |
+
The train/validation split keeps rows sharing a `scene_id` in `meta` on one side, and the assignment is frozen (`splits/` in the summary repository). S12 saw 7,570 rows under 1,251 keys. No validation input image has the content of a training input image, and none is a pixel-level near-copy of one. S12 does not record per source whether that test ran, but it skips it only for a source whose spec sets `split_leak_pixels: false`, and no spec does; over all sources it compared 21,661 candidate pairs (perceptual hash within 6 bits) pixel by pixel and found no near-copy (checked 2026-09-25).
|
| 188 |
+
|
| 189 |
+
### Answer-prior balancing (S13)
|
| 190 |
+
|
| 191 |
+
Each (task, split) group is checked separately. An answer is the answer value compared as lower-cased text without a trailing full stop, with 'farther' read as 'further' and 'nearer' as 'closer' (for multiple choice, the option text, not the letter; where the candidates are drawn in the image, as in zebra_jigsaw and zebra_tetris, the answer is the letter itself). An answer is real when it holds at least 5 rows and 2% of the group; k is the number of real answers. Answer step: the target is max(30%, 1/k) when k >= 2, and max(30%, 1/d) over the d distinct answers when k = 1; a validation group uses the larger of its own target and its task's train target. A group is cut only when k >= 1 and its most common answer holds more than the target plus 5 percentage points; every answer is then capped at one common count, chosen so that none exceeds the target, and smaller answers keep all their rows. At the answer step, a group at or below that trigger, or with no real answer (k = 0), is left as it is, so its most common answer can hold up to the target plus 5 percentage points. A task whose train group has exactly two real answers is instead cut, in every split, so that its two largest answers have equal counts, with no trigger. Rank and label steps: then, in a group where every option value of every row is a number, the rank of the correct option among the sorted values, and after it, in a group where every trained answer is an option label, the label, are each capped by the same cut-and-trigger rule on their own counts (own target, validation included): capped, never evened out, so two labels are cut only when one exceeds 55%, and then only down to 50%. These steps can also cut groups the answer step left whole, including k = 0 groups, and can raise an answer's final share above its target; the run fails if a real answer ends above the target plus 5 percentage points. A train group of at least 20 rows in which one answer holds 90% or more fails the run. PET (exact_cells_pet) instead cuts each (question type x turn direction) cell to equal counts of its two answers; a PET cell that shows only one answer is removed.
|
| 192 |
+
|
| 193 |
+
| task | split | pass | rule | rows in → out | real answers k | target | largest share, before → after | cut |
|
| 194 |
+
|---|---|---|---|---:|---:|---:|---:|---|
|
| 195 |
+
| anchor | train | answer | `cap30[canon]` | 690 → 690 | 0 | 30.0% | 1.4% → 1.4% | no |
|
| 196 |
+
| anchor | train | letter | `cap30[letter]` | 690 → 690 | 4 | 30.0% | 33.3% → 33.3% | no |
|
| 197 |
+
| anchor | validation | answer | `cap30[canon]` | 26 → 26 | 0 | 30.0% | 7.7% → 7.7% | no |
|
| 198 |
+
| anchor | validation | letter | `cap30[letter]` | 26 → 21 | 3 | 33.3% | 46.2% → 33.3% | yes |
|
| 199 |
+
| counting | train | answer | `cap30[canon]` | 1,098 → 1,098 | 6 | 30.0% | 30.3% → 30.3% | no |
|
| 200 |
+
| counting | train | rank | `cap30[rank]` | 1,098 → 590 | 4 | 30.0% | 39.0% → 30.0% | yes |
|
| 201 |
+
| counting | train | letter | `cap30[letter]` | 590 → 590 | 4 | 30.0% | 31.0% → 31.0% | no |
|
| 202 |
+
| counting | validation | answer | `cap30[canon]` | 43 → 43 | 3 | 33.3% | 30.2% → 30.2% | no |
|
| 203 |
+
| counting | validation | rank | `cap30[rank]` | 43 → 37 | 3 | 33.3% | 41.9% → 32.4% | yes |
|
| 204 |
+
| counting | validation | letter | `cap30[letter]` | 37 → 37 | 3 | 33.3% | 32.4% → 32.4% | no |
|
| 205 |
+
| perspective_taking | train | answer | `cap30[canon]` | 1,245 → 1,245 | 7 | 30.0% | 26.5% → 26.5% | no |
|
| 206 |
+
| perspective_taking | train | letter | `cap30[letter]` | 1,245 → 1,245 | 4 | 30.0% | 27.6% → 27.6% | no |
|
| 207 |
+
| perspective_taking | validation | answer | `cap30[canon]` | 49 → 49 | 5 | 30.0% | 26.5% → 26.5% | no |
|
| 208 |
+
| perspective_taking | validation | letter | `cap30[letter]` | 49 → 49 | 4 | 30.0% | 32.7% → 32.7% | no |
|
| 209 |
+
| relative_direction | train | answer | `cap30[canon]` | 1,399 → 1,399 | 7 | 30.0% | 27.2% → 27.2% | no |
|
| 210 |
+
| relative_direction | train | letter | `cap30[letter]` | 1,399 → 1,399 | 4 | 30.0% | 26.0% → 26.0% | no |
|
| 211 |
+
| relative_direction | validation | answer | `cap30[canon]` | 59 → 59 | 5 | 30.0% | 25.4% → 25.4% | no |
|
| 212 |
+
| relative_direction | validation | letter | `cap30[letter]` | 59 → 50 | 4 | 30.0% | 37.3% → 30.0% | yes |
|
| 213 |
+
| relative_distance | train | answer | `cap30[canon]` | 2,824 → 2,824 | 4 | 30.0% | 3.0% → 3.0% | no |
|
| 214 |
+
| relative_distance | train | letter | `cap30[letter]` | 2,824 → 2,824 | 4 | 30.0% | 34.5% → 34.5% | no |
|
| 215 |
+
| relative_distance | validation | answer | `cap30[canon]` | 137 → 137 | 11 | 30.0% | 9.5% → 9.5% | no |
|
| 216 |
+
| relative_distance | validation | letter | `cap30[letter]` | 137 → 110 | 4 | 30.0% | 37.2% → 30.0% | yes |
|
| 217 |
+
|
| 218 |
+
S13 removed 555 rows from this source.
|
| 219 |
+
|
| 220 |
+
### Text-only baselines
|
| 221 |
+
|
| 222 |
+
Accuracy of guessers that never see an image. For each task the released training rows are split into two fixed halves by a hash of `row_uid`; each guesser is fitted on one half and scored once on the other (one held-out half, not cross-validation; `eval rows` below). The reference is chance (the mean of 1 / number of options) where every row is multiple choice, and otherwise the eval-half accuracy of always giving the answer most common in the fit half (when a task's top answers are nearly tied, this need not be the task's most common answer; the line after the table gives that answer's validation score). Accuracies are recounted from the stored rates and `eval rows`, so they are exact. A task is flagged when a text-only guesser beats its reference by more than 0.15 (for a free-form task, a guesser other than the most common answer). A flagged task can be partly answered from the text alone; an unflagged task passed only these probes, which do not prove the text carries no answer. Report scores on every task next to this baseline.
|
| 223 |
+
|
| 224 |
+
Guessers: `keywords`: the most common answer per set of spatial words in the question; `last_mentioned`: the option named last in the question body; `letter_prior`: the most common answer letter; `majority`: the answer most common in the fit half; `numeric_offset`: where every option is a number, the smallest or largest option plus or minus the offset the correct one sat at most often (`last_mentioned` on the other rows); `option_prior`: the option text that won most often when shown; `option_rank`: where every option is a number, the option at the rank among the sorted values that the correct one held most often (`last_mentioned` on the other rows); `template`: the most common answer per question wording (numbers masked, object names kept).
|
| 225 |
+
|
| 226 |
+
| task | best text-only guesser | accuracy | reference | margin | eval rows | flagged |
|
| 227 |
+
|---|---|---:|---:|---:|---:|---|
|
| 228 |
+
| anchor | `option_prior` | 0.438 | 0.250 (chance) | +0.188 | 363 | **yes** |
|
| 229 |
+
| counting | `template` | 0.410 | 0.250 (chance) | +0.160 | 315 | **yes** |
|
| 230 |
+
| perspective_taking | `option_prior` | 0.451 | 0.250 (chance) | +0.201 | 625 | **yes** |
|
| 231 |
+
| relative_direction | `option_prior` | 0.399 | 0.250 (chance) | +0.149 | 712 | no |
|
| 232 |
+
| relative_distance | `option_prior` | 0.379 | 0.346 (chance) | +0.033 | 1,432 | no |
|
| 233 |
+
|
| 234 |
+
## Spot-check (S14)
|
| 235 |
+
|
| 236 |
+
**Pending.** The S14 rows are chosen and flagged `S14.sampled_qa` in `meta` and `preview`; the human pass over them has not been signed off yet.
|
| 237 |
+
|
| 238 |
+
## Citation
|
| 239 |
+
|
| 240 |
+
Please cite the upstream paper and Infinigen (https://github.com/princeton-vl/infinigen), as the VDrop card asks:
|
| 241 |
+
|
| 242 |
+
```bibtex
|
| 243 |
+
@article{yang2026and,
|
| 244 |
+
title={How and What to Imagine? Visual Thinking in Unified Multimodal Models for Cross-View Spatial Reasoning},
|
| 245 |
+
author={Yang, Qian and Sikarwar, Ankur and Le, Huy and Zhang, Le and Shi, Zhuan and Taslakian, Perouz and Agrawal, Aishwarya},
|
| 246 |
+
journal={arXiv preprint arXiv:2605.27310},
|
| 247 |
+
year={2026}
|
| 248 |
+
}
|
| 249 |
+
|
| 250 |
+
@inproceedings{infinigen2024indoors,
|
| 251 |
+
author={Raistrick, Alexander and Mei, Lingjie and Kayan, Karhan and Yan, David and Zuo, Yiming and Han, Beining and Wen, Hongyu and Parakh, Meenal and Alexandropoulos, Stamatis and Lipson, Lahav and Ma, Zeyu and Deng, Jia},
|
| 252 |
+
title={Infinigen Indoors: Photorealistic Indoor Scenes using Procedural Generation},
|
| 253 |
+
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
|
| 254 |
+
month={June},
|
| 255 |
+
year={2024},
|
| 256 |
+
pages={21783-21794}
|
| 257 |
+
}
|
| 258 |
+
|
| 259 |
+
@inproceedings{infinigen2023infinite,
|
| 260 |
+
title={Infinite Photorealistic Worlds Using Procedural Generation},
|
| 261 |
+
author={Raistrick, Alexander and Lipson, Lahav and Ma, Zeyu and Mei, Lingjie and Wang, Mingzhe and Zuo, Yiming and Kayan, Karhan and Wen, Hongyu and Han, Beining and Wang, Yihan and Newell, Alejandro and Law, Hei and Goyal, Ankit and Yang, Kaiyu and Deng, Jia},
|
| 262 |
+
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
|
| 263 |
+
pages={12630--12641},
|
| 264 |
+
year={2023}
|
| 265 |
+
}
|
| 266 |
+
```
|
| 267 |
+
|
| 268 |
+
## Provenance
|
| 269 |
+
|
| 270 |
+
The release files were written by our conversion code (the code repository is not public yet), `scripts/convert/export.py` at commit `4aaee1f4e946`, from build `vdrop_r2`. The build was made by `scripts/convert/run_source.py` from the same repository at commit `949af62f8ab7`. S10, S12 and S13 ran before the export; `reports/export_manifest.json` pins every input the export read by SHA-1 (`build_manifest_sha1`, `s10_keep_sha1`, `s12_assignments_sha1`, `s13_balanced_keep_sha1`).
|
| 271 |
+
|
| 272 |
+
Every row removed between upstream and this release has one line, with its reason, in `reports/`: `build/dropped.jsonl` (rows refused before conversion or dropped by a conversion step); `build/quarantine.jsonl` (rows set aside by S4c because an automatic check could not match the read-back's conclusion to the label); `s10_dropped.jsonl` (duplicates removed by S10); `s10_label_conflicts.jsonl` (rows S10 withheld because another row asks the identical question, options in the same order, of the same images with a different answer); `s13_dropped.jsonl` (rows removed by answer-prior balancing). `known_issues/` lists rows with a measured problem (see Known issues); `reports/` also holds the build manifest (absolute paths cut to basenames) and counters, the S14 sample list (`s14_sample.tsv`: row_uid, task, split) and `export_manifest.json`. Part of [`yrlyrl/spatial-mmcot`](https://huggingface.co/datasets/yrlyrl/spatial-mmcot).
|
meta/train/chunk_00000.parquet
ADDED
|
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version https://git-lfs.github.com/spec/v1
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size 424096
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meta/validation/chunk_00000.parquet
ADDED
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version https://git-lfs.github.com/spec/v1
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size 22115
|
parquet_info.json
ADDED
|
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|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
| 1 |
+
{
|
| 2 |
+
"vdrop/train/chunk_00000.parquet": {
|
| 3 |
+
"num_row_groups": 53,
|
| 4 |
+
"num_rows": 6748
|
| 5 |
+
},
|
| 6 |
+
"vdrop/validation/chunk_00000.parquet": {
|
| 7 |
+
"num_row_groups": 3,
|
| 8 |
+
"num_rows": 267
|
| 9 |
+
}
|
| 10 |
+
}
|
preview/train/chunk_00000.parquet
ADDED
|
@@ -0,0 +1,3 @@
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|
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|
|
|
|
|
|
|
|
|
|
| 1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:b6539888275754274b0443f4b80ee99294d87a929398c39d5ee54a3a70e45c11
|
| 3 |
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size 206103898
|
preview/validation/chunk_00000.parquet
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:74c1eb9bac4d077c9515bd9bea76f551d030e68e468675ae8f2beda945fc6dea
|
| 3 |
+
size 7834388
|
reports/build/counters.json
ADDED
|
@@ -0,0 +1,88 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"slug": "vdrop",
|
| 3 |
+
"targets_out": 7570,
|
| 4 |
+
"steps": {
|
| 5 |
+
"S0raw": {
|
| 6 |
+
"step": "S0raw",
|
| 7 |
+
"in_": 158,
|
| 8 |
+
"out": 0,
|
| 9 |
+
"dropped": 0,
|
| 10 |
+
"repaired": 0,
|
| 11 |
+
"quarantined": 0,
|
| 12 |
+
"rejected": 158,
|
| 13 |
+
"seconds": 0.0
|
| 14 |
+
},
|
| 15 |
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"S4": {
|
| 16 |
+
"step": "S4",
|
| 17 |
+
"in_": 7570,
|
| 18 |
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"out": 7570,
|
| 19 |
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"dropped": 0,
|
| 20 |
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"repaired": 0,
|
| 21 |
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"quarantined": 0,
|
| 22 |
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"rejected": 0,
|
| 23 |
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"seconds": 0.0604093074798584
|
| 24 |
+
},
|
| 25 |
+
"S4c": {
|
| 26 |
+
"step": "S4c",
|
| 27 |
+
"in_": 7570,
|
| 28 |
+
"out": 7570,
|
| 29 |
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"dropped": 0,
|
| 30 |
+
"repaired": 0,
|
| 31 |
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"quarantined": 0,
|
| 32 |
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"rejected": 0,
|
| 33 |
+
"seconds": 0.011017322540283203
|
| 34 |
+
},
|
| 35 |
+
"S5": {
|
| 36 |
+
"step": "S5",
|
| 37 |
+
"in_": 7570,
|
| 38 |
+
"out": 7570,
|
| 39 |
+
"dropped": 0,
|
| 40 |
+
"repaired": 0,
|
| 41 |
+
"quarantined": 0,
|
| 42 |
+
"rejected": 0,
|
| 43 |
+
"seconds": 0.007812738418579102
|
| 44 |
+
},
|
| 45 |
+
"S8": {
|
| 46 |
+
"step": "S8",
|
| 47 |
+
"in_": 7570,
|
| 48 |
+
"out": 7570,
|
| 49 |
+
"dropped": 0,
|
| 50 |
+
"repaired": 0,
|
| 51 |
+
"quarantined": 0,
|
| 52 |
+
"rejected": 0,
|
| 53 |
+
"seconds": 102.54547715187073
|
| 54 |
+
},
|
| 55 |
+
"S9": {
|
| 56 |
+
"step": "S9",
|
| 57 |
+
"in_": 7570,
|
| 58 |
+
"out": 7570,
|
| 59 |
+
"dropped": 0,
|
| 60 |
+
"repaired": 0,
|
| 61 |
+
"quarantined": 0,
|
| 62 |
+
"rejected": 0,
|
| 63 |
+
"seconds": 1.11020827293396
|
| 64 |
+
},
|
| 65 |
+
"S0": {
|
| 66 |
+
"step": "S0",
|
| 67 |
+
"in_": 7570,
|
| 68 |
+
"out": 7570,
|
| 69 |
+
"dropped": 0,
|
| 70 |
+
"repaired": 0,
|
| 71 |
+
"quarantined": 0,
|
| 72 |
+
"rejected": 0,
|
| 73 |
+
"seconds": 2.5707435607910156
|
| 74 |
+
}
|
| 75 |
+
},
|
| 76 |
+
"expectations": {
|
| 77 |
+
"S0raw.rejected": {
|
| 78 |
+
"want": 158,
|
| 79 |
+
"got": 158,
|
| 80 |
+
"ok": true
|
| 81 |
+
},
|
| 82 |
+
"S0.out": {
|
| 83 |
+
"want": 7570,
|
| 84 |
+
"got": 7570,
|
| 85 |
+
"ok": true
|
| 86 |
+
}
|
| 87 |
+
}
|
| 88 |
+
}
|
reports/build/dropped.jsonl
ADDED
|
@@ -0,0 +1,158 @@
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|
| 1 |
+
{"row_uid": "perspective_taking_000002", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 2 |
+
{"row_uid": "perspective_taking_000047", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 3 |
+
{"row_uid": "perspective_taking_000053", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 4 |
+
{"row_uid": "perspective_taking_000061", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 5 |
+
{"row_uid": "perspective_taking_000083", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 6 |
+
{"row_uid": "perspective_taking_000084", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 7 |
+
{"row_uid": "perspective_taking_000085", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 8 |
+
{"row_uid": "perspective_taking_000088", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 9 |
+
{"row_uid": "perspective_taking_000092", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 10 |
+
{"row_uid": "perspective_taking_000112", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 11 |
+
{"row_uid": "perspective_taking_000113", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 12 |
+
{"row_uid": "perspective_taking_000124", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 13 |
+
{"row_uid": "perspective_taking_000131", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 14 |
+
{"row_uid": "perspective_taking_000136", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 15 |
+
{"row_uid": "perspective_taking_000146", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 16 |
+
{"row_uid": "perspective_taking_000150", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 17 |
+
{"row_uid": "perspective_taking_000154", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 18 |
+
{"row_uid": "perspective_taking_000161", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 19 |
+
{"row_uid": "perspective_taking_000167", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 20 |
+
{"row_uid": "perspective_taking_000169", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 21 |
+
{"row_uid": "perspective_taking_000171", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 22 |
+
{"row_uid": "perspective_taking_000172", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 23 |
+
{"row_uid": "perspective_taking_000177", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 24 |
+
{"row_uid": "perspective_taking_000180", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 25 |
+
{"row_uid": "perspective_taking_000187", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 26 |
+
{"row_uid": "perspective_taking_000206", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 27 |
+
{"row_uid": "perspective_taking_000210", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 28 |
+
{"row_uid": "perspective_taking_000218", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 29 |
+
{"row_uid": "perspective_taking_000222", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 30 |
+
{"row_uid": "perspective_taking_000224", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 31 |
+
{"row_uid": "perspective_taking_000226", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 32 |
+
{"row_uid": "perspective_taking_000240", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 33 |
+
{"row_uid": "perspective_taking_000249", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 34 |
+
{"row_uid": "perspective_taking_000257", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 35 |
+
{"row_uid": "perspective_taking_000258", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 36 |
+
{"row_uid": "perspective_taking_000262", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 37 |
+
{"row_uid": "perspective_taking_000264", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 38 |
+
{"row_uid": "perspective_taking_000267", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 39 |
+
{"row_uid": "perspective_taking_000280", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 40 |
+
{"row_uid": "perspective_taking_000299", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 41 |
+
{"row_uid": "perspective_taking_000308", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 42 |
+
{"row_uid": "perspective_taking_000328", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 43 |
+
{"row_uid": "perspective_taking_000358", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 44 |
+
{"row_uid": "perspective_taking_000364", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 45 |
+
{"row_uid": "perspective_taking_000368", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 46 |
+
{"row_uid": "perspective_taking_000375", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 47 |
+
{"row_uid": "perspective_taking_000399", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 48 |
+
{"row_uid": "perspective_taking_000404", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 49 |
+
{"row_uid": "perspective_taking_000407", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 50 |
+
{"row_uid": "perspective_taking_000411", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 51 |
+
{"row_uid": "perspective_taking_000420", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 52 |
+
{"row_uid": "perspective_taking_000422", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 53 |
+
{"row_uid": "perspective_taking_000426", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 54 |
+
{"row_uid": "perspective_taking_000434", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 55 |
+
{"row_uid": "perspective_taking_000440", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 56 |
+
{"row_uid": "perspective_taking_000452", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 57 |
+
{"row_uid": "perspective_taking_000456", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 58 |
+
{"row_uid": "perspective_taking_000457", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 59 |
+
{"row_uid": "perspective_taking_000459", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 60 |
+
{"row_uid": "perspective_taking_000466", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 61 |
+
{"row_uid": "perspective_taking_000467", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 62 |
+
{"row_uid": "perspective_taking_000470", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 63 |
+
{"row_uid": "perspective_taking_000486", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 64 |
+
{"row_uid": "perspective_taking_000490", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 65 |
+
{"row_uid": "perspective_taking_000491", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 66 |
+
{"row_uid": "perspective_taking_000494", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 67 |
+
{"row_uid": "perspective_taking_000496", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 68 |
+
{"row_uid": "perspective_taking_000505", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 69 |
+
{"row_uid": "perspective_taking_000506", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 70 |
+
{"row_uid": "perspective_taking_000515", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 71 |
+
{"row_uid": "perspective_taking_000518", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 72 |
+
{"row_uid": "perspective_taking_000520", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 73 |
+
{"row_uid": "perspective_taking_000521", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 74 |
+
{"row_uid": "perspective_taking_000522", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 75 |
+
{"row_uid": "perspective_taking_000524", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 76 |
+
{"row_uid": "perspective_taking_000531", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 77 |
+
{"row_uid": "perspective_taking_000537", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 78 |
+
{"row_uid": "perspective_taking_000542", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 79 |
+
{"row_uid": "perspective_taking_000562", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 80 |
+
{"row_uid": "perspective_taking_000573", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 81 |
+
{"row_uid": "perspective_taking_000582", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 82 |
+
{"row_uid": "perspective_taking_000599", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 83 |
+
{"row_uid": "perspective_taking_000604", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 84 |
+
{"row_uid": "perspective_taking_000622", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 85 |
+
{"row_uid": "perspective_taking_000632", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 86 |
+
{"row_uid": "perspective_taking_000635", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 87 |
+
{"row_uid": "perspective_taking_000652", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 88 |
+
{"row_uid": "perspective_taking_000656", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 89 |
+
{"row_uid": "perspective_taking_000660", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 90 |
+
{"row_uid": "perspective_taking_000671", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 91 |
+
{"row_uid": "perspective_taking_000679", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 92 |
+
{"row_uid": "perspective_taking_000684", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 93 |
+
{"row_uid": "perspective_taking_000703", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 94 |
+
{"row_uid": "perspective_taking_000723", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 95 |
+
{"row_uid": "perspective_taking_000759", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 96 |
+
{"row_uid": "perspective_taking_000767", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 97 |
+
{"row_uid": "perspective_taking_000782", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 98 |
+
{"row_uid": "perspective_taking_000787", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 99 |
+
{"row_uid": "perspective_taking_000803", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 100 |
+
{"row_uid": "perspective_taking_000807", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 101 |
+
{"row_uid": "perspective_taking_000813", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 102 |
+
{"row_uid": "perspective_taking_000814", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 103 |
+
{"row_uid": "perspective_taking_000824", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 104 |
+
{"row_uid": "perspective_taking_000833", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 105 |
+
{"row_uid": "perspective_taking_000844", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 106 |
+
{"row_uid": "perspective_taking_000851", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 107 |
+
{"row_uid": "perspective_taking_000854", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 108 |
+
{"row_uid": "perspective_taking_000866", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 109 |
+
{"row_uid": "perspective_taking_000884", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 110 |
+
{"row_uid": "perspective_taking_000889", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 111 |
+
{"row_uid": "perspective_taking_000896", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 112 |
+
{"row_uid": "perspective_taking_000908", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 113 |
+
{"row_uid": "perspective_taking_000912", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 114 |
+
{"row_uid": "perspective_taking_000913", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 115 |
+
{"row_uid": "perspective_taking_000917", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 116 |
+
{"row_uid": "perspective_taking_000958", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 117 |
+
{"row_uid": "perspective_taking_000972", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 118 |
+
{"row_uid": "perspective_taking_000986", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 119 |
+
{"row_uid": "perspective_taking_000995", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 120 |
+
{"row_uid": "perspective_taking_001003", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 121 |
+
{"row_uid": "perspective_taking_001017", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 122 |
+
{"row_uid": "perspective_taking_001022", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 123 |
+
{"row_uid": "perspective_taking_001028", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 124 |
+
{"row_uid": "perspective_taking_001033", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 125 |
+
{"row_uid": "perspective_taking_001041", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 126 |
+
{"row_uid": "perspective_taking_001061", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 127 |
+
{"row_uid": "perspective_taking_001072", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 128 |
+
{"row_uid": "perspective_taking_001073", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 129 |
+
{"row_uid": "perspective_taking_001079", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 130 |
+
{"row_uid": "perspective_taking_001104", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 131 |
+
{"row_uid": "perspective_taking_001111", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 132 |
+
{"row_uid": "perspective_taking_001127", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 133 |
+
{"row_uid": "perspective_taking_001132", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 134 |
+
{"row_uid": "perspective_taking_001144", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 135 |
+
{"row_uid": "perspective_taking_001157", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 136 |
+
{"row_uid": "perspective_taking_001162", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 137 |
+
{"row_uid": "perspective_taking_001176", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 138 |
+
{"row_uid": "perspective_taking_001187", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 139 |
+
{"row_uid": "perspective_taking_001194", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 140 |
+
{"row_uid": "perspective_taking_001251", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 141 |
+
{"row_uid": "perspective_taking_001256", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 142 |
+
{"row_uid": "perspective_taking_001260", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 143 |
+
{"row_uid": "perspective_taking_001263", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 144 |
+
{"row_uid": "perspective_taking_001296", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 145 |
+
{"row_uid": "perspective_taking_001297", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 146 |
+
{"row_uid": "perspective_taking_001321", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 147 |
+
{"row_uid": "perspective_taking_001327", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 148 |
+
{"row_uid": "perspective_taking_001335", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 149 |
+
{"row_uid": "perspective_taking_001340", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 150 |
+
{"row_uid": "perspective_taking_001363", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 151 |
+
{"row_uid": "perspective_taking_001374", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 152 |
+
{"row_uid": "perspective_taking_001377", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 153 |
+
{"row_uid": "perspective_taking_001390", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 154 |
+
{"row_uid": "perspective_taking_001392", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 155 |
+
{"row_uid": "perspective_taking_001405", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 156 |
+
{"row_uid": "perspective_taking_001408", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 157 |
+
{"row_uid": "perspective_taking_001434", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
| 158 |
+
{"row_uid": "perspective_taking_001439", "step": "S0raw", "flag": "S3.partner_phrasing", "detail": "reference frame is 'your partner', which the two inputs do not identify", "rejected": true}
|
reports/build/manifest.json
ADDED
|
@@ -0,0 +1,40 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"slug": "vdrop",
|
| 3 |
+
"raw": "vdrop",
|
| 4 |
+
"out": "vdrop_r2",
|
| 5 |
+
"raw_rows_read": 7728,
|
| 6 |
+
"raw_unusable": 158,
|
| 7 |
+
"rows_out": 7570,
|
| 8 |
+
"targets_out": 7570,
|
| 9 |
+
"chunks": 1,
|
| 10 |
+
"row_groups": 60,
|
| 11 |
+
"seconds": 292.5,
|
| 12 |
+
"spec": {
|
| 13 |
+
"slug": "vdrop",
|
| 14 |
+
"num_inputs": 2,
|
| 15 |
+
"max_k": 1,
|
| 16 |
+
"source_scene_corpus": "infinigen",
|
| 17 |
+
"min_inputs": 2,
|
| 18 |
+
"license": "cc-by-4.0",
|
| 19 |
+
"license_commercial": true,
|
| 20 |
+
"replayable": false,
|
| 21 |
+
"min_image_edge": 256,
|
| 22 |
+
"copy_max_mean_diff": 2.0,
|
| 23 |
+
"copy_max_changed_frac": 0.01,
|
| 24 |
+
"allow_truncation": false,
|
| 25 |
+
"exclude_row_uids": "",
|
| 26 |
+
"exclude_row_uids_expected": 0,
|
| 27 |
+
"exclude_image_hashes": "",
|
| 28 |
+
"exclude_image_hashes_expected": 0,
|
| 29 |
+
"crop_black_bars": true,
|
| 30 |
+
"expected": {
|
| 31 |
+
"S0raw.rejected": 158,
|
| 32 |
+
"S0.out": 7570
|
| 33 |
+
}
|
| 34 |
+
},
|
| 35 |
+
"gates_failed": false,
|
| 36 |
+
"limit": 0,
|
| 37 |
+
"converter_git_sha": "949af62f8ab701c58da5cdeed32946242c571c83",
|
| 38 |
+
"converter_git_dirty": false,
|
| 39 |
+
"paths_redacted": "absolute build-machine paths cut to basenames for publication; build_manifest_sha1 in export_manifest.json is the original's sha1"
|
| 40 |
+
}
|
reports/build/quarantine.jsonl
ADDED
|
File without changes
|
reports/export_manifest.json
ADDED
|
@@ -0,0 +1,1767 @@
|
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|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
{
|
| 2 |
+
"source": "vdrop",
|
| 3 |
+
"build": "vdrop_r2",
|
| 4 |
+
"export_git_sha": "4aaee1f4e9469da41810aac049fa411c1834b809",
|
| 5 |
+
"export_git_dirty": false,
|
| 6 |
+
"limit": 0,
|
| 7 |
+
"converter_git_sha": "949af62f8ab701c58da5cdeed32946242c571c83",
|
| 8 |
+
"converter_git_dirty": false,
|
| 9 |
+
"build_counts": {
|
| 10 |
+
"raw_rows_read": 7728,
|
| 11 |
+
"raw_unusable": 158,
|
| 12 |
+
"rows_out": 7570
|
| 13 |
+
},
|
| 14 |
+
"build_manifest_sha1": "80a28b1facd7f58407ca87390e5197137fa0a847",
|
| 15 |
+
"s10_keep_sha1": "9fd27b7ff9e2be8d9e0466795f251875210de152",
|
| 16 |
+
"s12_assignments_sha1": "d0195205cd5852e08769b99f9c72740f8ce9fcaa",
|
| 17 |
+
"s13_balanced_keep_sha1": "af73ed4ef438de882feeee30de4a25701f2a3f6a",
|
| 18 |
+
"license": "cc-by-4.0",
|
| 19 |
+
"license_commercial": true,
|
| 20 |
+
"upstream_repo": "QianYangMILA/vdrop-crossview-8k",
|
| 21 |
+
"donor": null,
|
| 22 |
+
"visual_options": null,
|
| 23 |
+
"num_inputs": 2,
|
| 24 |
+
"max_k": 1,
|
| 25 |
+
"preview_max_k": 1,
|
| 26 |
+
"rows": {
|
| 27 |
+
"train": 6748,
|
| 28 |
+
"validation": 267
|
| 29 |
+
},
|
| 30 |
+
"targets": {
|
| 31 |
+
"train": 6748,
|
| 32 |
+
"validation": 267
|
| 33 |
+
},
|
| 34 |
+
"distinct_targets_total": 1158,
|
| 35 |
+
"num_input_images": {
|
| 36 |
+
"train": [
|
| 37 |
+
2,
|
| 38 |
+
2
|
| 39 |
+
],
|
| 40 |
+
"validation": [
|
| 41 |
+
2,
|
| 42 |
+
2
|
| 43 |
+
]
|
| 44 |
+
},
|
| 45 |
+
"num_steps": {
|
| 46 |
+
"train": [
|
| 47 |
+
1,
|
| 48 |
+
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|
| 49 |
+
],
|
| 50 |
+
"validation": [
|
| 51 |
+
1,
|
| 52 |
+
1
|
| 53 |
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]
|
| 54 |
+
},
|
| 55 |
+
"distinct_targets": {
|
| 56 |
+
"train": 1115,
|
| 57 |
+
"validation": 43
|
| 58 |
+
},
|
| 59 |
+
"supervision_kind": {
|
| 60 |
+
"train": {
|
| 61 |
+
"visual_only": 6748
|
| 62 |
+
},
|
| 63 |
+
"validation": {
|
| 64 |
+
"visual_only": 267
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"source_scene_corpus": {
|
| 68 |
+
"train": {
|
| 69 |
+
"infinigen": 6748
|
| 70 |
+
},
|
| 71 |
+
"validation": {
|
| 72 |
+
"infinigen": 267
|
| 73 |
+
}
|
| 74 |
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},
|
| 75 |
+
"filter_flags": {
|
| 76 |
+
"train": {
|
| 77 |
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"S5.replay_unsupported": 6748,
|
| 78 |
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"S14.sampled_qa": 194
|
| 79 |
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},
|
| 80 |
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"validation": {
|
| 81 |
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"S5.replay_unsupported": 267,
|
| 82 |
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"S14.sampled_qa": 6
|
| 83 |
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}
|
| 84 |
+
},
|
| 85 |
+
"answer_type": {
|
| 86 |
+
"train": {
|
| 87 |
+
"mcq_letter": 6748
|
| 88 |
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+
"side table located next to a light brown shelf": 1,
|
| 455 |
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"green desk lamp kept on a gray side table": 1,
|
| 456 |
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"gray side table on which a green lamp is kept": 1,
|
| 457 |
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"purple sofa near a dark brown cabinet": 1,
|
| 458 |
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"door located next to a white desk": 1,
|
| 459 |
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"purple sofa near a black center table": 1,
|
| 460 |
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"window near a plant container with beige color": 1,
|
| 461 |
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"light brown shelf located next to a beige plant container": 1,
|
| 462 |
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"oven located next to a gray plant container": 1,
|
| 463 |
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"fridge located next to a white door": 1,
|
| 464 |
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"oven located next to a red cabinet": 1,
|
| 465 |
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"cabinet near a sink": 1,
|
| 466 |
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"blue door located next to an oven": 1,
|
| 467 |
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"cabinet located next to a window with purple curtains": 1,
|
| 468 |
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|
| 469 |
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"oven near a window with brown curtains": 1,
|
| 470 |
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"gray plant container near a window with the purple curtains": 1,
|
| 471 |
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"shelf located near an oven": 1,
|
| 472 |
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"cabinet located next to a window with beige curtains": 1,
|
| 473 |
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"gray plant container near a window with green curtains": 1,
|
| 474 |
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"yellow cabinet located next to a window with brown curtains": 1,
|
| 475 |
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"sink located next to a window with brown curtains": 1,
|
| 476 |
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"oven located near a fridge": 1,
|
| 477 |
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"yellow shelf positioned next to a brown door": 1,
|
| 478 |
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"brown door next to a yellow shelf": 1,
|
| 479 |
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"blue shelf near a window with a red curtain": 1,
|
| 480 |
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"brown door located next to an oven": 1,
|
| 481 |
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"sink near an oven": 1,
|
| 482 |
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"pink plant container": 1,
|
| 483 |
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"shelf with light brown color": 1,
|
| 484 |
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"window next to a light brown shelf": 1,
|
| 485 |
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"purple plant container located next to a window with black curtains": 1,
|
| 486 |
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"beige door near a window with brown shutters": 1,
|
| 487 |
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"oven located near a gray plant container": 1,
|
| 488 |
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"oven near a plant container with a gray color": 1,
|
| 489 |
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"oven near a beige door": 1,
|
| 490 |
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"brown door next to a green cabinet": 1,
|
| 491 |
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"oven located next to a window with purple curtains": 1,
|
| 492 |
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"bed located next to a window with black curtains": 1,
|
| 493 |
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"window located next to an orange bed": 1,
|
| 494 |
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"lamp located near a window with black curtains": 1,
|
| 495 |
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"lamp near a window with black curtains": 1,
|
| 496 |
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"beige door located next to a green plant container": 1,
|
| 497 |
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"cabinet located next to a black desk": 1,
|
| 498 |
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"lamp located next to a green cabinet": 1,
|
| 499 |
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"green cabinet located next to a beige lamp": 1,
|
| 500 |
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"plant container kept on a light brown cabinet": 1,
|
| 501 |
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"plant container situated next to a bed with purple bedframe": 1,
|
| 502 |
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"purple bed near a small wall art": 1,
|
| 503 |
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"purple bed near a wall art piece": 1,
|
| 504 |
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"Cabinet near a window with brown shutters": 1,
|
| 505 |
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"bed with beige bedframe next to a wall art": 1,
|
| 506 |
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"floor lamp located next to a blue bed": 1,
|
| 507 |
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"bed with blue bedframe next to a beige lamp": 1,
|
| 508 |
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"bed with a black bedframe near a window with a black curtain": 1,
|
| 509 |
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"desk next to a bed with brown bedframe": 1,
|
| 510 |
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"desk located next to a bed with brown bedframe": 1,
|
| 511 |
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"wall art located next to a beige lamp": 1,
|
| 512 |
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"red shelf near a beige lamp": 1,
|
| 513 |
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"bed with beige bedframe located next to a brown side table": 1,
|
| 514 |
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"brown lamp located next to a brown side table": 1,
|
| 515 |
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"brown side table located next to a brown lamp": 1,
|
| 516 |
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"desk near a computer monitor": 1,
|
| 517 |
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"cabinet located next to a white door": 1,
|
| 518 |
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"floor lamp near a white door": 1,
|
| 519 |
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"lamp near a window with green curtains": 1,
|
| 520 |
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"gray lamp": 1,
|
| 521 |
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"green lamp": 1,
|
| 522 |
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"black bed near a plant container with gray color": 1,
|
| 523 |
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"brown bed near a gray lamp": 1,
|
| 524 |
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"green door": 1,
|
| 525 |
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"cabinet located next to a brown bed": 1,
|
| 526 |
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"door located next to a beige lamp": 1,
|
| 527 |
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"white cabinet positioned next to a beige lamp": 1,
|
| 528 |
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|
| 529 |
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"floor beige lamp near a container for brown plants": 1,
|
| 530 |
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|
| 531 |
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"bed with brown bedframe near a black lamp": 1,
|
| 532 |
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"bed near a pink lamp": 1,
|
| 533 |
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"window located next to a pink lamp": 1,
|
| 534 |
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"floor beige lamp near a bed with brown bedframe": 1,
|
| 535 |
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"bed with brown bedframe near a beige lamp": 1,
|
| 536 |
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"bed with olive bedframe located next to a black desk": 1,
|
| 537 |
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"white shelf near a window with brown curtains": 1,
|
| 538 |
+
"cabinet situated next to a beige lamp": 1,
|
| 539 |
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"wall art near a beige lamp": 1,
|
| 540 |
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"bed with beige bedframe next to a window with beige curtains": 1,
|
| 541 |
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"floor beige lamp near a white desk": 1,
|
| 542 |
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|
| 543 |
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"cabinet located next to a blue bed": 1,
|
| 544 |
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"door located next to a light brown cabinet": 1,
|
| 545 |
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"blue bed near a white door": 1,
|
| 546 |
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"lamp located next to a white door": 1,
|
| 547 |
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"window located next to a beige lamp": 1,
|
| 548 |
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|
| 549 |
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"plant container located next to a beige lamp": 1,
|
| 550 |
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|
| 551 |
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"computer monitor kept on a white desk": 1,
|
| 552 |
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|
| 553 |
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"green bed with green bedframe located next to a green lamp": 1,
|
| 554 |
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"window near a plant container with brown color": 1,
|
| 555 |
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|
| 556 |
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"wall art next to a red cabinet": 1,
|
| 557 |
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"window with blue curtains next to a red cabinet": 1,
|
| 558 |
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"door located next to a blue shelf": 1,
|
| 559 |
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"shelf next to a beige door": 1,
|
| 560 |
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"shelf on which a green plant container is kept": 1,
|
| 561 |
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"door located next to a red shelf": 1,
|
| 562 |
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"a door located next to a red shelf": 1,
|
| 563 |
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|
| 564 |
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"beige TV near a window with green curtains": 1,
|
| 565 |
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|
| 566 |
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|
| 567 |
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"cabinet near a black side table": 1,
|
| 568 |
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"side table near a beige door": 1,
|
| 569 |
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"shelf near a window with black curtains": 1,
|
| 570 |
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"black curtain window next to a yellow shelf": 1,
|
| 571 |
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|
| 572 |
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"desk on which a beige lamp is kept": 1,
|
| 573 |
+
"blue sofa next to a beige lamp": 1,
|
| 574 |
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"door near a brown TV": 1,
|
| 575 |
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"black door located next to a white sofa": 1,
|
| 576 |
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"brown center table next to a green sofa": 1,
|
| 577 |
+
"sofa located next to a brown center table": 1,
|
| 578 |
+
"green sofa next to a window with beige curtains": 1,
|
| 579 |
+
"green sofa near a window with beige curtains": 1,
|
| 580 |
+
"window with yellow shutters": 1,
|
| 581 |
+
"shelf located next to a beige plant container": 1,
|
| 582 |
+
"bed with brown bedframe near a black door": 1,
|
| 583 |
+
"black door near a bed with blue bedframe": 1,
|
| 584 |
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"floor beige lamp kept next to a dark brown shelf": 1,
|
| 585 |
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"computer monitor near a brown plant container": 1,
|
| 586 |
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"black desk located next to a blue door": 1,
|
| 587 |
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"bed with green bedframe next to a white desk": 1,
|
| 588 |
+
"white desk near a light brown shelf": 1,
|
| 589 |
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"bed with brown bedframe located next to a white door": 1,
|
| 590 |
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"desk near a window with black shutters": 1,
|
| 591 |
+
"brown plant container near a beige desk lamp": 1,
|
| 592 |
+
"white shelf located next to a beige lamp": 1,
|
| 593 |
+
"bed with brown bedframe near a computer monitor": 1,
|
| 594 |
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"lamp located next to a brown door": 1,
|
| 595 |
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|
| 596 |
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"gray toilet near a black door": 1,
|
| 597 |
+
"short yellow shelf": 1,
|
| 598 |
+
"gray toilet located next to a dark brown cabinet": 1,
|
| 599 |
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"white door near a window with white shutters": 1,
|
| 600 |
+
"yellow shelf next to a purple plant container": 1,
|
| 601 |
+
"purple plant container located next to a yellow shelf": 1,
|
| 602 |
+
"gray toilet located next to a light brown shelf": 1,
|
| 603 |
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"shelf located next to a gold sink": 1,
|
| 604 |
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"shelf near a brown plant container": 1,
|
| 605 |
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"tall green plant container": 1,
|
| 606 |
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"cyan door located next to a white cabinet": 1,
|
| 607 |
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"cabinet located next to a gray toilet": 1,
|
| 608 |
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"black door located near a light brown cabinet": 1,
|
| 609 |
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"beige sink located next to a black door": 1,
|
| 610 |
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"gray toilet located next to a blue cabinet": 1,
|
| 611 |
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"bed with yellow bedframe": 1,
|
| 612 |
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"bed with brown bedframe situated next to a window with beige curtains": 1,
|
| 613 |
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"bed located next to a white desk": 1,
|
| 614 |
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"plant container located near a brown door": 1,
|
| 615 |
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"shelf located next to a brown door": 1,
|
| 616 |
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"black door located next to a white shelf": 1,
|
| 617 |
+
"green sink located next to a black plant container": 1,
|
| 618 |
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"pink door located next to a light brown shelf": 1,
|
| 619 |
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"grey sink located next to a dark brown cabinet": 1,
|
| 620 |
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"window located near a light brown shelf": 1,
|
| 621 |
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"wall art located near a gray plant container": 1,
|
| 622 |
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"beige door located next to a bed with brown bedframe": 1,
|
| 623 |
+
"red cabinet located next to a window with beige curtains": 1,
|
| 624 |
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"window with the black curtains located next to a red shelf": 1,
|
| 625 |
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"black door located next to a white cabinet": 1,
|
| 626 |
+
"oven located next to a dark brown shelf": 1,
|
| 627 |
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"brown door located next to a window with beige curtains": 1,
|
| 628 |
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"window with the beige curtains": 1,
|
| 629 |
+
"fridge located next to a grey plant container": 1,
|
| 630 |
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"purple sofa located next to a blue shelf": 1,
|
| 631 |
+
"brown sofa located next to a red shelf": 1,
|
| 632 |
+
"pink sofa": 1,
|
| 633 |
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"window with the gray curtains": 1,
|
| 634 |
+
"black door located next to a yellow shelf": 1,
|
| 635 |
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"gray sofa": 1,
|
| 636 |
+
"window with orange shutters": 1,
|
| 637 |
+
"desk located next to a window with beige curtains": 1,
|
| 638 |
+
"black plant container located next to a white shelf": 1,
|
| 639 |
+
"green sofa located near a brown door": 1,
|
| 640 |
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"purple sofa": 1,
|
| 641 |
+
"purple sofa located next to a gray centre table": 1,
|
| 642 |
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"window located near a black desk": 1,
|
| 643 |
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"bed with black bedframe located next to a beige door": 1,
|
| 644 |
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"window located next to a white desk": 1,
|
| 645 |
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"desk located next to a window with brown curtains": 1,
|
| 646 |
+
"bed with brown bedframe located next to a brown door": 1,
|
| 647 |
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"white door located next to a green shelf": 1,
|
| 648 |
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"grey shelf": 1,
|
| 649 |
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"bed with an orange bedframe": 1,
|
| 650 |
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"white desk located next to a computer monitor": 1,
|
| 651 |
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"white cabinet located next to a window with white curtains": 1,
|
| 652 |
+
"bed with brown bedframe near a window with white curtains": 1,
|
| 653 |
+
"brown cabinet": 1,
|
| 654 |
+
"bed with beige bedframe": 1,
|
| 655 |
+
"window with the grey curtains": 1,
|
| 656 |
+
"white desk located next to a bed with white bedframe": 1,
|
| 657 |
+
"short shelf": 1,
|
| 658 |
+
"bed with green bedframe located next to a white shelf": 1,
|
| 659 |
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"oven located next to a white cabinet": 1,
|
| 660 |
+
"oven located next to a black cabinet": 1,
|
| 661 |
+
"black dishwasher located near a white cabinet": 1,
|
| 662 |
+
"tall cabinet": 1,
|
| 663 |
+
"white cabinet located next to a window with grey curtains": 1,
|
| 664 |
+
"window with grey curtains": 1,
|
| 665 |
+
"black door located next to an oven": 1,
|
| 666 |
+
"oven located next to a black shelf": 1,
|
| 667 |
+
"shelf located next to an oven": 1,
|
| 668 |
+
"window located next to a green dishwasher": 1,
|
| 669 |
+
"shelf located near a window with beige curtains": 1,
|
| 670 |
+
"oven located near a window with green curtains": 1,
|
| 671 |
+
"fridge located next to a blue shelf": 1,
|
| 672 |
+
"green door located near a white cabinet": 1,
|
| 673 |
+
"white cabinet located near a green door": 1,
|
| 674 |
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"window located near an oven": 1,
|
| 675 |
+
"black dishwasher located near a window with white curtains": 1,
|
| 676 |
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"shelf located near a white cabinet": 1,
|
| 677 |
+
"oven located next to a white shelf": 1,
|
| 678 |
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"oven located next to a wall art": 1,
|
| 679 |
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"oven located next to a brown shelf": 1,
|
| 680 |
+
"white desk located next to a bed with black bedframe": 1
|
| 681 |
+
},
|
| 682 |
+
"anchor/validation": {
|
| 683 |
+
"gray plant container located next to a red cabinet": 2,
|
| 684 |
+
"computer monitor near a beige lamp": 2,
|
| 685 |
+
"cabinet located next to a beige lamp": 2,
|
| 686 |
+
"red cabinet located next to a gray plant container": 1,
|
| 687 |
+
"yellow shelf": 1,
|
| 688 |
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"wall art near a blue cabinet": 1,
|
| 689 |
+
"shelf located next to a beige door": 1,
|
| 690 |
+
"black door located next to a gray lamp": 1,
|
| 691 |
+
"purple lamp": 1,
|
| 692 |
+
"blue lamp": 1,
|
| 693 |
+
"door located next to a red cabinet": 1,
|
| 694 |
+
"cabinet near a small wall art": 1,
|
| 695 |
+
"wall art near a red cabinet": 1,
|
| 696 |
+
"side table near a window with beige curtains": 1,
|
| 697 |
+
"dark brown shelf": 1,
|
| 698 |
+
"window located next to a grey lamp": 1,
|
| 699 |
+
"grey door": 1,
|
| 700 |
+
"bed located next to a brown shelf": 1
|
| 701 |
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},
|
| 702 |
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"counting/train": {
|
| 703 |
+
"1": 177,
|
| 704 |
+
"2": 138,
|
| 705 |
+
"3": 108,
|
| 706 |
+
"4": 94,
|
| 707 |
+
"5": 58,
|
| 708 |
+
"6": 11,
|
| 709 |
+
"7": 4
|
| 710 |
+
},
|
| 711 |
+
"counting/validation": {
|
| 712 |
+
"1": 11,
|
| 713 |
+
"2": 9,
|
| 714 |
+
"3": 8,
|
| 715 |
+
"5": 3,
|
| 716 |
+
"4": 3,
|
| 717 |
+
"6": 2,
|
| 718 |
+
"7": 1
|
| 719 |
+
},
|
| 720 |
+
"perspective_taking/train": {
|
| 721 |
+
"left": 330,
|
| 722 |
+
"right": 303,
|
| 723 |
+
"front-left": 221,
|
| 724 |
+
"front-right": 196,
|
| 725 |
+
"front": 115,
|
| 726 |
+
"behind-left": 40,
|
| 727 |
+
"behind-right": 31,
|
| 728 |
+
"behind": 9
|
| 729 |
+
},
|
| 730 |
+
"perspective_taking/validation": {
|
| 731 |
+
"front-right": 13,
|
| 732 |
+
"left": 10,
|
| 733 |
+
"front-left": 9,
|
| 734 |
+
"right": 9,
|
| 735 |
+
"front": 6,
|
| 736 |
+
"behind-left": 1,
|
| 737 |
+
"behind-right": 1
|
| 738 |
+
},
|
| 739 |
+
"relative_direction/train": {
|
| 740 |
+
"left": 381,
|
| 741 |
+
"right": 342,
|
| 742 |
+
"front-left": 245,
|
| 743 |
+
"front-right": 223,
|
| 744 |
+
"front": 116,
|
| 745 |
+
"behind-right": 42,
|
| 746 |
+
"behind-left": 38,
|
| 747 |
+
"behind": 12
|
| 748 |
+
},
|
| 749 |
+
"relative_direction/validation": {
|
| 750 |
+
"right": 13,
|
| 751 |
+
"left": 13,
|
| 752 |
+
"front-right": 8,
|
| 753 |
+
"front-left": 7,
|
| 754 |
+
"front": 5,
|
| 755 |
+
"behind-right": 3,
|
| 756 |
+
"behind-left": 1
|
| 757 |
+
},
|
| 758 |
+
"relative_distance/train": {
|
| 759 |
+
"brown door": 85,
|
| 760 |
+
"window with the brown curtains": 73,
|
| 761 |
+
"window with the beige curtains": 63,
|
| 762 |
+
"white shelf": 60,
|
| 763 |
+
"black door": 49,
|
| 764 |
+
"white desk": 48,
|
| 765 |
+
"white door": 44,
|
| 766 |
+
"window with the green curtains": 35,
|
| 767 |
+
"dark brown cabinet": 34,
|
| 768 |
+
"tall shelf": 34,
|
| 769 |
+
"black plant container": 33,
|
| 770 |
+
"brown plant container": 33,
|
| 771 |
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"window with the black curtains": 32,
|
| 772 |
+
"window with brown shutters": 32,
|
| 773 |
+
"dark brown shelf": 32,
|
| 774 |
+
"short shelf": 31,
|
| 775 |
+
"window with beige curtains": 30,
|
| 776 |
+
"tall cabinet": 30,
|
| 777 |
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"blue shelf": 30,
|
| 778 |
+
"window with brown curtains": 28,
|
| 779 |
+
"green shelf": 28,
|
| 780 |
+
"green cabinet": 27,
|
| 781 |
+
"window with the white curtains": 26,
|
| 782 |
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"white cabinet": 22,
|
| 783 |
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"window with the grey curtains": 21,
|
| 784 |
+
"bed with brown bedframe": 21,
|
| 785 |
+
"yellow cabinet": 19,
|
| 786 |
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"grey plant container": 19,
|
| 787 |
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"window with black curtains": 18,
|
| 788 |
+
"brown door located next to a light brown shelf": 17,
|
| 789 |
+
"window with the purple curtains": 17,
|
| 790 |
+
"brown cabinet": 17,
|
| 791 |
+
"short white shelf": 16,
|
| 792 |
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"window with green curtains": 15,
|
| 793 |
+
"brown door located next to an oven": 15,
|
| 794 |
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"gray plant container": 14,
|
| 795 |
+
"light brown shelf": 14,
|
| 796 |
+
"door located next to a white shelf": 14,
|
| 797 |
+
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| 1110 |
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|
| 1599 |
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| 1600 |
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|
| 1601 |
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|
| 1602 |
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|
| 1603 |
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|
| 1604 |
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|
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|
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| 1627 |
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|
| 1628 |
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| 1629 |
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|
| 1630 |
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|
| 1631 |
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|
| 1632 |
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|
| 1633 |
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|
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|
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|
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|
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|
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|
| 1641 |
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|
| 1642 |
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|
| 1643 |
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|
| 1644 |
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|
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|
| 1646 |
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|
| 1647 |
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|
| 1648 |
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|
| 1649 |
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|
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|
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|
| 1653 |
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|
| 1654 |
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|
| 1655 |
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}
|
| 1656 |
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},
|
| 1657 |
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|
| 1658 |
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|
| 1659 |
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|
| 1660 |
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|
| 1661 |
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|
| 1662 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
| 1671 |
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|
| 1672 |
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|
| 1673 |
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|
| 1674 |
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|
| 1675 |
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}
|
| 1676 |
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},
|
| 1677 |
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|
| 1678 |
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|
| 1679 |
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|
| 1680 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
| 1689 |
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|
| 1690 |
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|
| 1691 |
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|
| 1692 |
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|
| 1693 |
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|
| 1694 |
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"last_mentioned_rule": "plain; no option mentioned on 315 of 315 eval rows -> letter prior A",
|
| 1695 |
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|
| 1696 |
+
"option_rank_rule": "rank 1 (0 = smallest)",
|
| 1697 |
+
"numeric_offset_rule": "max - 2"
|
| 1698 |
+
}
|
| 1699 |
+
},
|
| 1700 |
+
"vdrop_r2/perspective_taking": {
|
| 1701 |
+
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|
| 1702 |
+
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|
| 1703 |
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|
| 1704 |
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|
| 1705 |
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|
| 1706 |
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|
| 1707 |
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|
| 1708 |
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|
| 1709 |
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|
| 1710 |
+
"best": "option_prior",
|
| 1711 |
+
"flag": true,
|
| 1712 |
+
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|
| 1713 |
+
"sigma3": 0.06,
|
| 1714 |
+
"details": {
|
| 1715 |
+
"last_mentioned_rule": "plain; no option mentioned on 625 of 625 eval rows -> letter prior C",
|
| 1716 |
+
"last_mentioned_no_mention_eval_rows": 625
|
| 1717 |
+
}
|
| 1718 |
+
},
|
| 1719 |
+
"vdrop_r2/relative_direction": {
|
| 1720 |
+
"probed": true,
|
| 1721 |
+
"majority": 0.2374,
|
| 1722 |
+
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|
| 1723 |
+
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|
| 1724 |
+
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|
| 1725 |
+
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|
| 1726 |
+
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|
| 1727 |
+
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|
| 1728 |
+
"reference": "chance",
|
| 1729 |
+
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|
| 1730 |
+
"flag": false,
|
| 1731 |
+
"eval_rows": 712,
|
| 1732 |
+
"sigma3": 0.0562,
|
| 1733 |
+
"details": {
|
| 1734 |
+
"last_mentioned_rule": "plain; no option mentioned on 712 of 712 eval rows -> letter prior C",
|
| 1735 |
+
"last_mentioned_no_mention_eval_rows": 712
|
| 1736 |
+
}
|
| 1737 |
+
},
|
| 1738 |
+
"vdrop_r2/relative_distance": {
|
| 1739 |
+
"probed": true,
|
| 1740 |
+
"majority": 0.0293,
|
| 1741 |
+
"keywords": 0.0279,
|
| 1742 |
+
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|
| 1743 |
+
"option_prior": 0.3792,
|
| 1744 |
+
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|
| 1745 |
+
"last_mentioned": 0.3331,
|
| 1746 |
+
"chance": 0.3465,
|
| 1747 |
+
"reference": "chance",
|
| 1748 |
+
"best": "option_prior",
|
| 1749 |
+
"flag": false,
|
| 1750 |
+
"eval_rows": 1432,
|
| 1751 |
+
"sigma3": 0.0396,
|
| 1752 |
+
"details": {
|
| 1753 |
+
"last_mentioned_rule": "plain; no option mentioned on 1371 of 1432 eval rows -> letter prior B",
|
| 1754 |
+
"last_mentioned_no_mention_eval_rows": 1371
|
| 1755 |
+
}
|
| 1756 |
+
}
|
| 1757 |
+
},
|
| 1758 |
+
"s13_rule": "Each (task, split) group is checked separately. An answer is the answer value compared as lower-cased text without a trailing full stop, with 'farther' read as 'further' and 'nearer' as 'closer' (for multiple choice, the option text, not the letter; where the candidates are drawn in the image, as in zebra_jigsaw and zebra_tetris, the answer is the letter itself). An answer is real when it holds at least 5 rows and 2% of the group; k is the number of real answers. Answer step: the target is max(30%, 1/k) when k >= 2, and max(30%, 1/d) over the d distinct answers when k = 1; a validation group uses the larger of its own target and its task's train target. A group is cut only when k >= 1 and its most common answer holds more than the target plus 5 percentage points; every answer is then capped at one common count, chosen so that none exceeds the target, and smaller answers keep all their rows. At the answer step, a group at or below that trigger, or with no real answer (k = 0), is left as it is, so its most common answer can hold up to the target plus 5 percentage points. A task whose train group has exactly two real answers is instead cut, in every split, so that its two largest answers have equal counts, with no trigger. Rank and label steps: then, in a group where every option value of every row is a number, the rank of the correct option among the sorted values, and after it, in a group where every trained answer is an option label, the label, are each capped by the same cut-and-trigger rule on their own counts (own target, validation included): capped, never evened out, so two labels are cut only when one exceeds 55%, and then only down to 50%. These steps can also cut groups the answer step left whole, including k = 0 groups, and can raise an answer's final share above its target; the run fails if a real answer ends above the target plus 5 percentage points. A train group of at least 20 rows in which one answer holds 90% or more fails the run. PET (exact_cells_pet) instead cuts each (question type x turn direction) cell to equal counts of its two answers; a PET cell that shows only one answer is removed.",
|
| 1759 |
+
"s13_constants": {
|
| 1760 |
+
"TARGET": 0.3,
|
| 1761 |
+
"TRIGGER_GAP": 0.05,
|
| 1762 |
+
"MIN_CLASS_ROWS": 5,
|
| 1763 |
+
"MIN_CLASS_SHARE": 0.02,
|
| 1764 |
+
"PURE_PRIOR": 0.9,
|
| 1765 |
+
"BLIND_MARGIN": 0.15
|
| 1766 |
+
}
|
| 1767 |
+
}
|
reports/known_issues/summary.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{}
|
reports/s10_dropped.jsonl
ADDED
|
File without changes
|
reports/s10_label_conflicts.jsonl
ADDED
|
File without changes
|
reports/s13_dropped.jsonl
ADDED
|
@@ -0,0 +1,555 @@
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|
| 1 |
+
{"build": "vdrop_r2", "row_uid": "6309b6ac6f367a32", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "wall art near a blue cabinet"}
|
| 2 |
+
{"build": "vdrop_r2", "row_uid": "c324997154cb05b2", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "floor beige lamp"}
|
| 3 |
+
{"build": "vdrop_r2", "row_uid": "6aac8c551e369af3", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "blue shelf located near a window with blue curtains"}
|
| 4 |
+
{"build": "vdrop_r2", "row_uid": "dbc74e548941cffe", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "dark brown shelf"}
|
| 5 |
+
{"build": "vdrop_r2", "row_uid": "e6e99fccdd9a773b", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "brown shelf"}
|
| 6 |
+
{"build": "vdrop_r2", "row_uid": "0a9afc446358af81", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 7 |
+
{"build": "vdrop_r2", "row_uid": "8ef7d07fe2514217", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "4"}
|
| 8 |
+
{"build": "vdrop_r2", "row_uid": "6971c6caa10f880c", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 9 |
+
{"build": "vdrop_r2", "row_uid": "159c20ba2fe07fb9", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 10 |
+
{"build": "vdrop_r2", "row_uid": "f76602b7d9523991", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "3"}
|
| 11 |
+
{"build": "vdrop_r2", "row_uid": "b006e65e8e84279f", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 12 |
+
{"build": "vdrop_r2", "row_uid": "a371a1a455606c24", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "1"}
|
| 13 |
+
{"build": "vdrop_r2", "row_uid": "95552383e284595e", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "5"}
|
| 14 |
+
{"build": "vdrop_r2", "row_uid": "91ca36c20b515dd1", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "4"}
|
| 15 |
+
{"build": "vdrop_r2", "row_uid": "2949ba34af4271f0", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 16 |
+
{"build": "vdrop_r2", "row_uid": "944c926ece9ef861", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 17 |
+
{"build": "vdrop_r2", "row_uid": "add3d0fbd93e7d45", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 18 |
+
{"build": "vdrop_r2", "row_uid": "ce1ebe766715c93f", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 19 |
+
{"build": "vdrop_r2", "row_uid": "c2c13d54915fe708", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 20 |
+
{"build": "vdrop_r2", "row_uid": "f45ef24d5255ccda", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "5"}
|
| 21 |
+
{"build": "vdrop_r2", "row_uid": "0676e52f73dacda5", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "7"}
|
| 22 |
+
{"build": "vdrop_r2", "row_uid": "7d0bd6f2c2d3b27c", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "3"}
|
| 23 |
+
{"build": "vdrop_r2", "row_uid": "40460c1438f1bb34", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 24 |
+
{"build": "vdrop_r2", "row_uid": "9035a853009b27dd", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "3"}
|
| 25 |
+
{"build": "vdrop_r2", "row_uid": "8b7fa7002f783bbb", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 26 |
+
{"build": "vdrop_r2", "row_uid": "d6c38979e0ddc312", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 27 |
+
{"build": "vdrop_r2", "row_uid": "b748d48412fc5433", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "3"}
|
| 28 |
+
{"build": "vdrop_r2", "row_uid": "5fa4c385df9caac3", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "3"}
|
| 29 |
+
{"build": "vdrop_r2", "row_uid": "de6251983a138e3c", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "4"}
|
| 30 |
+
{"build": "vdrop_r2", "row_uid": "52db79d51b00b8fa", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "5"}
|
| 31 |
+
{"build": "vdrop_r2", "row_uid": "4e53ef93dc104b54", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "1"}
|
| 32 |
+
{"build": "vdrop_r2", "row_uid": "efdc54a37325cd8e", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "4"}
|
| 33 |
+
{"build": "vdrop_r2", "row_uid": "0341b1dfd4cfcac7", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 34 |
+
{"build": "vdrop_r2", "row_uid": "ee5e57e4ec879f07", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "4"}
|
| 35 |
+
{"build": "vdrop_r2", "row_uid": "9777708b34c608b9", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "3"}
|
| 36 |
+
{"build": "vdrop_r2", "row_uid": "1749b2496330bc0e", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "4"}
|
| 37 |
+
{"build": "vdrop_r2", "row_uid": "e1789841a80cd916", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 38 |
+
{"build": "vdrop_r2", "row_uid": "894377c548485f07", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "4"}
|
| 39 |
+
{"build": "vdrop_r2", "row_uid": "c5a9421acded487a", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "3"}
|
| 40 |
+
{"build": "vdrop_r2", "row_uid": "ea4061efd8a0d038", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "3"}
|
| 41 |
+
{"build": "vdrop_r2", "row_uid": "e9dc4b8e141b0d73", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "4"}
|
| 42 |
+
{"build": "vdrop_r2", "row_uid": "ad2de188d065539d", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "3"}
|
| 43 |
+
{"build": "vdrop_r2", "row_uid": "d858777462673497", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "4"}
|
| 44 |
+
{"build": "vdrop_r2", "row_uid": "3b598a10f3c39948", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "3"}
|
| 45 |
+
{"build": "vdrop_r2", "row_uid": "35af6b87ed2f143e", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 46 |
+
{"build": "vdrop_r2", "row_uid": "d3d748d7b11814a0", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 47 |
+
{"build": "vdrop_r2", "row_uid": "5e351fa10badde37", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 48 |
+
{"build": "vdrop_r2", "row_uid": "773bc0b27a115987", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 49 |
+
{"build": "vdrop_r2", "row_uid": "103b8aabca649f5a", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 50 |
+
{"build": "vdrop_r2", "row_uid": "979e4559ca5f934a", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "3"}
|
| 51 |
+
{"build": "vdrop_r2", "row_uid": "1bb0e83c99957a0c", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "2"}
|
| 52 |
+
{"build": "vdrop_r2", "row_uid": "79d0c2b52ffd1e25", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "3"}
|
| 53 |
+
{"build": "vdrop_r2", "row_uid": "0d622f796721516b", "split": "train", "reason": "prior_downsample", "step": "rank", "answer": "5"}
|
| 54 |
+
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{"build": "vdrop_r2", "row_uid": "f16e3b1357cad104", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "yellow shelf"}
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{"build": "vdrop_r2", "row_uid": "5d80292b9a972829", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "purple plant container"}
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{"build": "vdrop_r2", "row_uid": "3b8c576741e42e5d", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "green shelf"}
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{"build": "vdrop_r2", "row_uid": "91d6431ef7430c7a", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "green shelf"}
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{"build": "vdrop_r2", "row_uid": "adaa3bc5bcfb157b", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "dark brown shelf"}
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{"build": "vdrop_r2", "row_uid": "9111ad01cb8ccd20", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "white desk located next to a grey plant container"}
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{"build": "vdrop_r2", "row_uid": "0587b871cb56e482", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "desk located near a window with orange shutters"}
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{"build": "vdrop_r2", "row_uid": "93a54bdaff08bdf5", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "window with the brown curtains"}
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| 538 |
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{"build": "vdrop_r2", "row_uid": "190979abb4e471cb", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "dark brown shelf"}
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| 539 |
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{"build": "vdrop_r2", "row_uid": "08aa10762792df98", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "window with the brown curtains"}
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| 540 |
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{"build": "vdrop_r2", "row_uid": "8ef6e4a1520a1a3a", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "grey floor lamp"}
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| 541 |
+
{"build": "vdrop_r2", "row_uid": "ee18fb2ec3fb9209", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "window with the grey curtains near a yellow shelf"}
|
| 542 |
+
{"build": "vdrop_r2", "row_uid": "9b9339d383f7cc3f", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "black desk located next to a yellow shelf"}
|
| 543 |
+
{"build": "vdrop_r2", "row_uid": "a8535981d4c083b5", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "brown door"}
|
| 544 |
+
{"build": "vdrop_r2", "row_uid": "5eba8aabc5ed2402", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "brown door"}
|
| 545 |
+
{"build": "vdrop_r2", "row_uid": "fcab8940e9471dc5", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "grey floor lamp"}
|
| 546 |
+
{"build": "vdrop_r2", "row_uid": "eb3de4c83e4e9b96", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "red shelf located next to a window with blue curtains"}
|
| 547 |
+
{"build": "vdrop_r2", "row_uid": "943c80ff8cc110e0", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "brown door"}
|
| 548 |
+
{"build": "vdrop_r2", "row_uid": "77ebd364cfb81470", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "grey floor lamp"}
|
| 549 |
+
{"build": "vdrop_r2", "row_uid": "428167abcbfb524f", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "window with the grey curtains"}
|
| 550 |
+
{"build": "vdrop_r2", "row_uid": "b8def6b21d081ab0", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "yellow sofa"}
|
| 551 |
+
{"build": "vdrop_r2", "row_uid": "c4381ace6528f4bc", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "bed with green bedframe"}
|
| 552 |
+
{"build": "vdrop_r2", "row_uid": "ff301087b1e92af3", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "bed with green bedframe"}
|
| 553 |
+
{"build": "vdrop_r2", "row_uid": "a55e472506c15379", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "white shelf"}
|
| 554 |
+
{"build": "vdrop_r2", "row_uid": "d9f85147f248c388", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "tan shelf"}
|
| 555 |
+
{"build": "vdrop_r2", "row_uid": "1b15baf2602dcede", "split": "val", "reason": "prior_downsample", "step": "letter", "answer": "tan shelf"}
|
reports/s14_sample.tsv
ADDED
|
@@ -0,0 +1,201 @@
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|
| 1 |
+
# row_uid task split -- the S14 spot-check rows; also flagged S14.sampled_qa
|
| 2 |
+
62b2f08bc7e0c905 relative_distance train
|
| 3 |
+
0c99e7d22f518b44 relative_distance train
|
| 4 |
+
7e676858e7b7c050 anchor train
|
| 5 |
+
c9517d0cb79ace2c relative_distance train
|
| 6 |
+
266e1bde201d11cf relative_distance train
|
| 7 |
+
507177d6b78d0c59 relative_distance train
|
| 8 |
+
3e15bca8ca49abcb relative_distance train
|
| 9 |
+
407d78caf039fa09 relative_distance train
|
| 10 |
+
c4f2e8c82fbcbebe relative_direction train
|
| 11 |
+
27e2bf4a47bb651f relative_distance train
|
| 12 |
+
9152d56a3a31b541 relative_distance train
|
| 13 |
+
b7099af3fd3eebc3 perspective_taking train
|
| 14 |
+
ad49bd0f963b7197 perspective_taking train
|
| 15 |
+
05182d328ce60927 perspective_taking train
|
| 16 |
+
8b0b0babbd2681b2 relative_direction train
|
| 17 |
+
0dd8c82f5ebd607d relative_distance train
|
| 18 |
+
b27e1c6f52f089cd relative_direction train
|
| 19 |
+
655a691b8d50c75f relative_direction train
|
| 20 |
+
e19c4f21dfdaffee relative_distance train
|
| 21 |
+
ef7ea30334541a41 relative_distance validation
|
| 22 |
+
e0feaf6051cbfa1c relative_distance train
|
| 23 |
+
f5ffc46404250747 relative_distance train
|
| 24 |
+
ae6983d301fe7231 relative_direction train
|
| 25 |
+
b5bee6053c9e32a2 counting train
|
| 26 |
+
ef335a627fec2a94 relative_direction train
|
| 27 |
+
1b3caf663dadfe1f relative_distance train
|
| 28 |
+
ac292b052d42de32 anchor train
|
| 29 |
+
8c48891e164fbf1d relative_distance train
|
| 30 |
+
d5efb9ffa3564d6e perspective_taking train
|
| 31 |
+
f9092355725dbd68 relative_distance train
|
| 32 |
+
424d9768d73eec9d counting train
|
| 33 |
+
ba3242e99b2f8f6e counting train
|
| 34 |
+
47de6a4eb96d91d9 perspective_taking train
|
| 35 |
+
510b550d2c5a9738 perspective_taking validation
|
| 36 |
+
d6fec7cd0e7aa391 counting train
|
| 37 |
+
02d2ebfeba91f0af perspective_taking train
|
| 38 |
+
f3849c086d485b1c anchor train
|
| 39 |
+
a121950ece3ad1c0 relative_distance train
|
| 40 |
+
06997434a8132a56 relative_direction train
|
| 41 |
+
b260af3b323ff9a8 relative_distance train
|
| 42 |
+
2f42e8008c606a18 relative_direction train
|
| 43 |
+
f455d2933a755234 relative_direction train
|
| 44 |
+
89a06524b7813b70 relative_distance train
|
| 45 |
+
c8f49951094e1032 relative_direction train
|
| 46 |
+
d496a4df26b909d9 anchor train
|
| 47 |
+
f8b5dbf5e1b2cf54 relative_direction train
|
| 48 |
+
45e3eebd8ff04166 relative_distance train
|
| 49 |
+
96e56c5e42e47d04 relative_distance train
|
| 50 |
+
673ad07982271f9d counting train
|
| 51 |
+
58f20df0d6342239 relative_distance train
|
| 52 |
+
4e134c27d964a029 relative_direction train
|
| 53 |
+
d8f20cddf86f4baf relative_direction train
|
| 54 |
+
69a8bde3af3a9c07 relative_distance train
|
| 55 |
+
24a6f405a44935dc anchor train
|
| 56 |
+
676e913a6bb32b59 counting train
|
| 57 |
+
6423b6c0c96723f8 perspective_taking train
|
| 58 |
+
1b5685aedc544add relative_direction train
|
| 59 |
+
b1ecfd0f3e9cd466 anchor train
|
| 60 |
+
c28632156bbc156e relative_direction train
|
| 61 |
+
e4a01af03cf10e49 relative_direction train
|
| 62 |
+
458acb5aec3052ba relative_distance train
|
| 63 |
+
8e1ac12cc8ccd360 counting train
|
| 64 |
+
100131b3929b6e96 perspective_taking train
|
| 65 |
+
6208a4cb4a97cbc0 relative_direction train
|
| 66 |
+
21222e686f2bfbdb perspective_taking train
|
| 67 |
+
0e3ff538a085a052 perspective_taking train
|
| 68 |
+
e251329626428f15 relative_distance train
|
| 69 |
+
d54da1f0010303e8 relative_direction train
|
| 70 |
+
7911f38181f68817 relative_distance train
|
| 71 |
+
0b1ad0f8b3cf160c perspective_taking train
|
| 72 |
+
f11b87c5a60c07ef relative_direction train
|
| 73 |
+
1c71f95570e110d8 relative_direction train
|
| 74 |
+
80d4afe1838bb70f relative_direction train
|
| 75 |
+
4f08f74afdc4f178 relative_distance train
|
| 76 |
+
5f6e6a40bb0f751a counting train
|
| 77 |
+
7a7f3025ca57bd86 relative_distance train
|
| 78 |
+
8bab4bce9924548d relative_distance train
|
| 79 |
+
dad278917dc666cb relative_distance train
|
| 80 |
+
4c2978193975fa97 relative_distance train
|
| 81 |
+
a82c8aaac41f3a8c perspective_taking train
|
| 82 |
+
733230881f899597 relative_direction train
|
| 83 |
+
a6efd038fc8107a2 counting train
|
| 84 |
+
d219089d308864f6 counting validation
|
| 85 |
+
83ba6a7fa9f37c3a relative_direction train
|
| 86 |
+
d6b808f2a0097daa counting train
|
| 87 |
+
10f996fbaddb373e relative_distance train
|
| 88 |
+
04c0f0af95ecd527 relative_distance validation
|
| 89 |
+
26e81a2e844e4acb counting train
|
| 90 |
+
7920e13294177e5a counting train
|
| 91 |
+
a911c6225138d564 relative_direction train
|
| 92 |
+
fe40471fb5dbf5c0 relative_distance train
|
| 93 |
+
8c95a8fc93a8975d relative_distance train
|
| 94 |
+
e304ce2675d9c79f counting train
|
| 95 |
+
0bb1b786d904990c relative_direction train
|
| 96 |
+
9f2f0018d67b804d relative_distance train
|
| 97 |
+
16c15fb4cefb8d42 relative_distance train
|
| 98 |
+
b41537f6f795e2bd relative_distance train
|
| 99 |
+
34635ea96b932f00 perspective_taking train
|
| 100 |
+
f779274a18447d1d counting train
|
| 101 |
+
ecc23c00893cecd7 relative_distance train
|
| 102 |
+
1680d4f02ff4d95c perspective_taking train
|
| 103 |
+
dd1f12c716a945ed relative_distance train
|
| 104 |
+
14da95281d3e16e9 relative_direction train
|
| 105 |
+
41c45ece660ff8f3 relative_distance train
|
| 106 |
+
12c1de6a1efea6e3 relative_distance train
|
| 107 |
+
1535b75a72384932 relative_direction validation
|
| 108 |
+
cae63e53e46d614e counting train
|
| 109 |
+
31fb10b14ea75073 perspective_taking train
|
| 110 |
+
c3fedfceaad6f8ab perspective_taking train
|
| 111 |
+
a37a9e074509fe97 relative_distance train
|
| 112 |
+
ad10151663375b85 relative_distance train
|
| 113 |
+
0bb0fc5f91339d73 perspective_taking train
|
| 114 |
+
d58ae4f8b9387ce8 relative_direction train
|
| 115 |
+
7b742daddef6c6cc relative_distance train
|
| 116 |
+
2a964bc54cdf607c relative_direction train
|
| 117 |
+
889c139a8f7fe3de anchor train
|
| 118 |
+
954ade9fa6663586 relative_distance train
|
| 119 |
+
76ea681cdca55c26 relative_distance train
|
| 120 |
+
4d88cf319b26f61d relative_distance train
|
| 121 |
+
77adcf708a60bb63 counting train
|
| 122 |
+
97b3d06f5eb90cf7 relative_distance train
|
| 123 |
+
9406dd4dc7621591 relative_distance train
|
| 124 |
+
13e0a398f7ce23f6 anchor train
|
| 125 |
+
30dc2c96f79bb0ea relative_distance train
|
| 126 |
+
f6957877cb6648c2 relative_direction train
|
| 127 |
+
b318f95973ec6c2c relative_distance train
|
| 128 |
+
2d9ea8939c7f9eb2 relative_distance train
|
| 129 |
+
78258fb33c31a2b6 relative_distance train
|
| 130 |
+
7b466af1acf3e4c4 relative_distance train
|
| 131 |
+
8592944f0ef6823e relative_distance train
|
| 132 |
+
9045965888cb0823 perspective_taking train
|
| 133 |
+
e1e181678e0665dc perspective_taking train
|
| 134 |
+
2c49407904969450 relative_distance train
|
| 135 |
+
8e5f423e0107aad1 relative_distance train
|
| 136 |
+
d04b34c3935933a2 anchor train
|
| 137 |
+
6636a0f041376737 relative_distance train
|
| 138 |
+
deeb384a6b1fd2b4 relative_distance train
|
| 139 |
+
c6c610a8736171b8 perspective_taking train
|
| 140 |
+
50a1d0c14fda772e perspective_taking train
|
| 141 |
+
2373802c15869164 relative_distance train
|
| 142 |
+
ab3c0a8c0be0523b relative_distance train
|
| 143 |
+
9929debb4a94aa4c relative_direction train
|
| 144 |
+
a4a8d93d0e83a047 relative_distance train
|
| 145 |
+
77b6b2a3cead3548 relative_distance train
|
| 146 |
+
fc607a85b7391229 relative_direction train
|
| 147 |
+
176534add934e008 relative_distance train
|
| 148 |
+
a26faba163fcb909 relative_distance train
|
| 149 |
+
06bd7474a3870c03 counting train
|
| 150 |
+
29d64a54a6741221 perspective_taking train
|
| 151 |
+
703d8499c8317be2 anchor train
|
| 152 |
+
281474924c1a8951 relative_distance train
|
| 153 |
+
252e23c6b0f2eff5 relative_distance train
|
| 154 |
+
56aa18cc1dbee32a perspective_taking train
|
| 155 |
+
20f2506958109c03 perspective_taking train
|
| 156 |
+
e08b9404adcbf0ad relative_distance train
|
| 157 |
+
1f965dfb41761d32 relative_distance train
|
| 158 |
+
c3594cd8f5504e9c relative_distance train
|
| 159 |
+
1993dc1545a7a908 relative_distance train
|
| 160 |
+
cb85d08cc840aa0f relative_distance train
|
| 161 |
+
03d8543405341399 anchor train
|
| 162 |
+
12ad764e98c484f7 perspective_taking train
|
| 163 |
+
14da15e044fc5231 anchor train
|
| 164 |
+
78de554ddb7f559f relative_direction train
|
| 165 |
+
035febf319de8539 relative_direction train
|
| 166 |
+
614aaf04e3ca5f9b relative_distance train
|
| 167 |
+
7ba8e63951f7d63f relative_direction train
|
| 168 |
+
a94d494ad04e5146 relative_distance train
|
| 169 |
+
cc6ed4da7dd307d2 relative_distance train
|
| 170 |
+
bd089063484f1ed6 relative_direction train
|
| 171 |
+
8eba9fa1f7d086da relative_distance train
|
| 172 |
+
40aa7f2cbae27ef5 relative_distance train
|
| 173 |
+
efc3487673eb661c relative_direction train
|
| 174 |
+
ac39c99f8774feaf relative_direction train
|
| 175 |
+
a013e61d0f3b31af relative_distance train
|
| 176 |
+
041be981091a7ffa anchor validation
|
| 177 |
+
914a2c12b09be68b perspective_taking train
|
| 178 |
+
3aa1c0a0ca8a39cb perspective_taking train
|
| 179 |
+
1ec7f62447116464 perspective_taking train
|
| 180 |
+
8e403e6d0cb4ad77 relative_distance train
|
| 181 |
+
a137f8fdffe43480 anchor train
|
| 182 |
+
543df82dbd981835 perspective_taking train
|
| 183 |
+
67ae8fc0481a1e3e relative_direction train
|
| 184 |
+
07461832e60c358b relative_distance train
|
| 185 |
+
d8ab73cdb9bd60b6 relative_direction train
|
| 186 |
+
de9a31a95033ff57 relative_distance train
|
| 187 |
+
37dd6fa62e64497d relative_distance train
|
| 188 |
+
5d83d7ca86802a93 relative_direction train
|
| 189 |
+
e28775008dfe4d09 anchor train
|
| 190 |
+
533117830bd6bc28 perspective_taking train
|
| 191 |
+
91c14f1da760822e perspective_taking train
|
| 192 |
+
59bd0ecd1907ee97 anchor train
|
| 193 |
+
4122faa592ac270e anchor train
|
| 194 |
+
6b28dd6cf8819e55 perspective_taking train
|
| 195 |
+
a6f0e4ca19ec431c perspective_taking train
|
| 196 |
+
c530f564a5edcab5 perspective_taking train
|
| 197 |
+
31a6f72cd9078e41 perspective_taking train
|
| 198 |
+
f57c31161cd6bdaa perspective_taking train
|
| 199 |
+
0dcdd416d652ac9b anchor train
|
| 200 |
+
53e55fa98291ce28 anchor train
|
| 201 |
+
91f5a42de0dbf70b anchor train
|
train/chunk_00000.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1b8b07be753a00aafee8feedb7e8ca3cfb60bee0c6fe6194452effb29b88a358
|
| 3 |
+
size 190603319
|
validation/chunk_00000.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b85bf481a7de97e36c79d7e14cd89361ca313e2fdda471c906c41eb847d3c8f8
|
| 3 |
+
size 7149020
|