Dataset card: paper title, overview figure and links to main; difficulty splits follow the paper's scene tiers

splits/{easy,medium,hard}.{parquet,csv,txt} now list all three phases of each tier scene (99/99/102 rows), identical to manifests/<task>/<tier>.json and the paper (33/33/34 scenes). The previous per-phase label is kept in the phase_difficulty column. The card adds the paper title, authors, overview figure (paper Fig. 1) and citation, and links GitHub main.

README.md

@@ -1,6 +1,6 @@
 ---
 license: cc-by-4.0
-pretty_name: "RPX: Robot Perception X"
+pretty_name: "RPX: Same Scene, Different Story"
 task_categories:
 - image-segmentation
 - depth-estimation
@@ -22,7 +22,7 @@ size_categories:
 - 100K<n<1M
 configs:
 - config_name: multi_object
-  description: "MOS phase records split by Effort-Stratified Difficulty."
+  description: "MOS scene-phases grouped by the paper's difficulty tiers (33 Easy, 33 Medium, 34 Hard scenes, all three phases each)."
   default: true
   data_files:
   - split: easy
@@ -80,194 +80,209 @@ configs:
     path: tracking/metadata/text_initialization_v1/scene_condition_vocab.parquet
 ---
 
-# RPX: Robot Perception X
+<div align="center">
 
-RPX is a real-world RGB-D benchmark for measuring robot perception across scene changes. The canonical `naren/all` release combines the multi-object, egocentric, single-object, VQA, and tracking metadata that previously lived on separate dataset branches.
+# RPX: Same Scene, Different Story
 
-- **Code and benchmark toolkit:** [github.com/IRVLUTD/RPX](https://github.com/IRVLUTD/RPX/tree/naren/all)
-- **Recommended dataset revision:** `naren/all` (pin the commit SHA printed by your download for reproducible results)
-- **License:** [Creative Commons Attribution 4.0](https://creativecommons.org/licenses/by/4.0/)
+### Evaluating Robot Perception Across Scene Phases in the Wild
 
-<video controls muted loop playsinline width="100%" src="https://huggingface.co/datasets/IRVLUTD/RPX/resolve/main/assets/rpx-jumbotron.mp4"></video>
+**Jishnu Jaykumar P\*, Itay Kadosh\*, Narendhiran Vijayakumar\*, Srinanditha Kamath, Sai Haneesh Allu,<br>
+Govind Tyagi Rangappa, Animesh Maheshwari, Jikai Wang, Yu Xiang**<br>
+<sub>UT Dallas · UF Gainesville · IIIT Hyderabad · UT Austin   \*equal contribution</sub>
 
-## What is included
+[**Toolkit docs**](https://irvlutd.github.io/RPX/toolkit-docs/) ·
+[**Code**](https://github.com/IRVLUTD/RPX) ·
+[**`pip install rpx-benchmark`**](https://pypi.org/project/rpx-benchmark/) ·
+[**Docker images**](https://hub.docker.com/r/irvlutd/rpx) ·
+[**Cite**](#citation)
 
-| Collection | Coverage | Published modalities |
-|---|---:|---|
-| Multi-object scenes (MOS) | 100 scenes × 3 phases × 250 frames = 75,000 frames | RGB, depth, fisheye stereo, 6-DoF camera pose, instance masks and annotation metadata |
-| Egocentric views (Ego) | 100 scenes, 23,121 indexed frames | RGB, instance masks and tracking metadata |
-| Single-object scans (SOS) | 70 selected objects × 500 frames = 35,000 frames | RGB, depth, fisheye stereo, camera pose, masks and annotation metadata |
-| VQA ground truth | 4,529,322 single-image questions | attribute, spatial-bbox and spatial-binary tasks |
-| In-context VQA | 749,083 two-image questions | MOS/Ego attribute transfer and MOS spatial reasoning |
-| Runnable VQA subset | 31,500 questions, plus a 104-row acceptance set | deterministic bbox-output evaluation manifests |
-| Tracking text vocabulary | 70 objects and 2,739 scene-condition entries | stable IDs, canonical names and fixed text prompts |
+</div>
 
-`manifest/frames_v1.parquet` is the canonical frame inventory. It contains 133,121 unique frame records and accurately marks absent Ego depth, fisheye, pose and auxiliary-mask modalities. `manifest/frames_v2.parquet` is retained only as historical metadata; it is incomplete and is not referenced by the active release index.
+![RPX overview: the same real scenes captured before (Clutter), during (Interaction) and after (Clean) manipulation plus an egocentric view; six perception tasks evaluated through one pipeline; 100 multi-object scenes in three difficulty tiers; 70 single objects; RGB-D, stereo, pose, masks and questionnaires](https://huggingface.co/datasets/IRVLUTD/RPX/resolve/main/assets/rpx-overview.jpg)
 
-## Get started
+**A robot's scene does not hold still.** A tabletop starts cluttered, a hand rearranges it, and it ends clean.
+RPX captures **the same real scenes before, during and after manipulation** on robot-grade sensors, and scores
+six perception tasks on them through one pipeline. Two numbers summarize every model on a common scale:
+**phase robustness Φ** (does quality hold as the scene changes?) and **worst-phase quality 𝒥<sub>min</sub>**
+(how good is it at its weakest?).
 
-Install the benchmark package from the matching code branch:
+| 100 | 3 + 1 | 70 | 133,121 | 6 | 65 |
+|:---:|:---:|:---:|:---:|:---:|:---:|
+| real scenes<br><sub>50 indoor · 50 outdoor · 20 sites</sub> | phases + ego view<br><sub>Clutter · Interaction · Clean · Ego</sub> | everyday objects<br><sub>each scanned alone in 360°</sub> | annotated frames<br><sub>RGB-D · stereo · pose · masks</sub> | tasks<br><sub>depth to grounding</sub> | zero-shot evaluations |
 
-```bash
-git clone --branch naren/all https://github.com/IRVLUTD/RPX.git
-cd RPX
-python3 -m venv .venv
-. .venv/bin/activate
-python -m pip install -e './benchmark[hub,schemas]'
-hf auth login
-```
+**What it shows.** Depth and tracking change significantly across phases (p < 0.001); model rankings shift
+across tasks, prompting modes and input modalities on the very same scenes; and no evaluation reaches both
+𝒥<sub>min</sub> ≥ 0.75 and Φ ≥ 0.86.
 
-Download only the files needed for one task:
+## Six tasks, one pipeline
 
-```python
-from rpx_benchmark.dataset_hub import download_for_task
+| | Task | Scored on | Native metrics | Reference runtime |
+|---|---|---|---|---|
+| T1 | Image depth | 3 phases | AbsRel · RMSE · SILog · δ<sub>1</sub> | `irvlutd/rpx:depth-zipdepth-latest` |
+| T2 | Video depth | 3 phases | T1 metrics + TGM · TGSE | `irvlutd/rpx:depth-zipdepth-latest` |
+| T3 | Multi-object tracking | 3 phases + Ego | HOTA · MOTA · IDSW | `irvlutd/rpx:tracking-dam4sam-rpx-latest` |
+| T4 | Relative camera pose | Clutter · Clean | AUC@5° · @10° · @20° | `irvlutd/rpx:rcpe-monst3r-rpx-latest` |
+| T5 | VQA localization | 3 phases + Ego | Acc@0.5 · GIoU · Center-in-GT | `irvlutd/rpx:vqa-vllm` |
+| T6 | In-context VQA | 3 phases + Ego | Acc@0.5 · GIoU · Center-in-GT | `irvlutd/rpx:vqa-vllm` |
 
-result = download_for_task(
-    task="segmentation",
-    split="easy",
-    repo_id="IRVLUTD/RPX",
-    revision="naren/all",
-)
-print(result.local_dir, result.matched_scenes)
-```
+Every task keeps its field's native metrics; each result is mapped onto the same scene–phase scale, from which Φ
+and 𝒥<sub>min</sub> are computed. The [Docker guide](https://github.com/IRVLUTD/RPX/blob/main/docker/README.md#which-image-to-pull)
+lists which image holds which model.
 
-Equivalent CLI:
+## Get started in two minutes
 
 ```bash
-python -m rpx_benchmark.dataset_hub.cli download \
-  --task segmentation --split easy \
-  --repo-id IRVLUTD/RPX --revision naren/all
-```
-
-Do not clone or download the complete 237 GB repository for a single benchmark. The toolkit reads the task manifest and fetches the required shards.
-
-## How RPX works
+python -m pip install 'rpx-benchmark[hub,schemas]'
 
-Each MOS scene is captured in three phases:
-
-| Phase | Directory | Meaning |
-|---|---:|---|
-| Clutter | `0` / `phase0` | objects in a difficult, unorganized state |
-| Interaction | `1` / `phase1` | a person interacts with the scene |
-| Clean | `2` / `phase2` | the same scene after organization |
-
-The **Effort-Stratified Difficulty (ESD)** protocol assigns each of the 300 scene phases to `easy`, `medium`, or `hard` using the perception difficulty and annotation effort recorded in `splits/scene_splits.json`. Use the phase-level files in `splits/` for MOS evaluation. Ego captures do not participate in ESD.
-
-Task manifests under `manifests/<task>/<split>.json` identify samples and modalities. The benchmark downloads the corresponding archive shards, runs a reference model or user adapter, and reports task performance, robustness across phases, and compute cost. VQA uses its deterministic JSONL manifests and keeps answers inside the evaluator.
-
-## Tasks
-
-| Task | Inputs | Ground truth or output target |
-|---|---|---|
-| Monocular and video depth | RGB image or clip | metric depth |
-| Stereo depth | fisheye pair | metric depth |
-| Instance segmentation | RGB, optionally depth | instance mask |
-| Object detection / open-vocabulary detection | RGB, optionally text | boxes, labels and scores |
-| Object tracking | RGB clip, optionally a fixed text vocabulary | persistent object tracks |
-| Relative camera pose | two RGB or RGB-D frames | relative rotation and translation |
-| Visual grounding | RGB and text | object box |
-| VQA | one target image, or SOS reference crop plus target image | label and/or target-image box |
-| SOS templates | single-object RGB-D scan | object appearance, pose and mask library |
-
-The code package exposes ten generic task runners and a separate VQA evaluator. See the [benchmark README](https://github.com/IRVLUTD/RPX/blob/naren/all/benchmark/README.md) for the supplied pretrained-model commands and environments.
-
-## Bring your own model or API
+# Check the install on all six tasks with synthetic data (CPU, no downloads):
+python -m rpx_benchmark.examples.benchmark_tasks --task all --smoke --output results/six-task-smoke
+```
 
-Custom models use the same loaders, splits and metrics as supplied models. Wrap a local model or remote inference service with a benchmark factory:
+**Score your own model.** Wrap any callable, local model or API, and the toolkit fetches only the shards that
+task needs, runs it, and writes the metrics:
 
 ```python
 import rpx_benchmark as rpx
-from my_model import predict_depth  # RGB ndarray -> metric-depth ndarray
+from my_model import predict_depth          # RGB array -> depth in metres
 
 model = rpx.make_numpy_depth_model(predict_depth, name="my-depth")
 result, report, paths = rpx.run_monocular_depth(
     rpx.MonocularDepthRunConfig(
-        model=model,
-        split="easy",
-        repo_id="IRVLUTD/RPX",
-        revision="naren/all",
+        model=model, split="easy", repo_id="IRVLUTD/RPX",
         output_dir="rpx_results/my-depth/easy",
     )
 )
 print(result.aggregated)
 ```
 
-The package also provides factories and public runners for video depth, segmentation, detection, open-vocabulary detection, tracking, grounding, relative pose, sparse depth, and keypoint matching. Your wrapper owns authentication and service-specific configuration; the evaluator records predictions, errors, timing and provenance.
+The same pattern covers video depth, tracking, relative pose and VQA; see the
+[toolkit docs](https://irvlutd.github.io/RPX/toolkit-docs/). The toolkit pins a dataset commit by default
+(`rpx.DEFAULT_REVISION`), so two runs always read identical data. **Do not download the whole 237 GB
+repository** for one task.
 
-For VQA, provide a callable with this contract:
+**Just browse the data:**
 
 ```python
-from rpx_benchmark.vqa import evaluate_vqa
-
-# predict_vqa(image_paths, prompt, max_new_tokens, output_kind) -> raw text
-report = evaluate_vqa(
-    "vqa_manifest.jsonl",
-    predict_vqa,
-    model_name="my-vlm",
-    model_revision="my-checkpoint-or-api-version",
-    image_cache=".cache/rpx-vqa",
-    output_dir="rpx_results/my-vlm",
-)
-```
+from datasets import load_dataset
 
-Return one JSON object with `label` and `bbox`, where bbox is XYXY normalized to 0–1000; binary questions return `yes` or `no`. For two-image questions, `image_paths` is ordered `(reference_crop, target)`, and the predicted box always refers to the target image. The evaluator never passes ground-truth answers or boxes to the model callable.
+easy = load_dataset("IRVLUTD/RPX", "multi_object", split="easy")   # the paper's Easy tier: 33 scenes × 3 phases
+objects = load_dataset("IRVLUTD/RPX", "single_object", split="objects")
+```
 
-## Identity and join rules
+## Difficulty tiers
 
-RPX uses separate identifiers because objects and masks have different scopes:
+Scenes are stratified into **Easy (33), Medium (33) and Hard (34)** by annotation-refinement effort, the number
+of correction passes a scene's masks needed, as a proxy for perceptual ambiguity. A tier always contains **all
+three phases of its scenes**, exactly as in the paper and in every benchmark manifest.
 
-| Field | Scope and rule |
+| File | Contents |
 |---|---|
-| `global_object_id` | canonical integer identity; unique and contiguous from 1 through 70 |
-| `source_catalog_id` | original catalog identity stored as a string; dotted values such as `"18.2"` must remain strings |
-| `object_id` | canonical SOS folder key, for example `boot.2` |
-| `local_mask_id` / `mask_index` | local to a scene condition; join with scene, view and phase before mapping to a global ID |
-| `sample_id` | stable identifier for a VQA record; never derive object identity from it |
+| `splits/{easy,medium,hard}.parquet` (`.csv`, `.txt`) | one row per scene–phase of that tier: 99 / 99 / 102 rows |
+| `splits/scene_splits.json` | scene lists per tier, scores, per-phase scores, and scoring provenance |
+| `manifests/<task>/<tier>.json` | the exact samples each benchmark task evaluates |
+
+Each split row also keeps the phase's own score (`rpx_ds`) and the tier that score alone would give
+(`phase_difficulty`), for analyses at phase level. Ego captures are not scored for difficulty; they follow
+their scene's tier.
 
-The source of truth is `manifest/object_catalog_v1.json`. Join MOS masks through `manifest/mos_mask_object_map_v1.parquet`; join tracking prompts through `tracking/metadata/text_initialization_v1/scene_condition_vocab.parquet`. Questionnaire paths and SOS paths in the object catalog resolve to the same canonical object.
+## What is included
+
+| Collection | Coverage | Modalities |
+|---|---:|---|
+| Multi-object scenes (MOS) | 100 scenes × 3 phases × 250 frames = **75,000** | RGB, metric depth, fisheye stereo, 6-DoF pose, instance masks, tracklets |
+| Egocentric view (Ego) | 100 Interaction clips, **23,121** frames | RGB (1920 × 1080), instance masks, tracklets |
+| Single-object scans (SOS) | 70 objects × 500 frames = **35,000** | RGB, metric depth, fisheye stereo, pose, masks, questionnaire |
+| VQA question bank | **4,529,322** single-image + **749,083** two-image records | attribute, spatial-box and spatial-binary questions |
+| Benchmark VQA subset | 31,500 runnable questions | fixed, seeded evaluation manifests |
+| Tracking text prompts | 70 objects, 2,739 scene–condition entries | stable IDs, canonical names, fixed prompts |
 
-## Repository layout
+Capture: Intel RealSense D435 (RGB-D, 640 × 480) with a T265 (fisheye stereo and visual–inertial pose) on a
+hand-held rig, plus a head-mounted GoPro during Interaction. Every object keeps one global identity across
+phases, views and the single-object catalogue.
+
+<details>
+<summary><b>Repository layout and file formats</b></summary>
 
 ```text
 assets/                         dataset-card media
 manifest/                       canonical inventories, object catalog and checksums
-manifests/<task>/<split>.json   executable benchmark sample manifests
+manifests/<task>/<tier>.json    executable benchmark sample manifests
 objects/                        70 single-object scan shard trees
 objects_meta/                   per-object metadata and questionnaires
 preview/                        lightweight MOS and Ego previews
 scenes/                         100 MOS phase trees and 100 Ego view trees
-splits/                         ESD phase tables and scene-level provenance
+splits/                         difficulty tiers and scoring provenance
 tracking/metadata/              text-initialization vocabularies and checksums
 vqa/                            full VQA Parquets and benchmark JSONL manifests
 ```
 
-Large modalities are packed in tar shards. RGB and fisheye imagery use lossless WebP members; depth and masks use PNG; camera poses are `(7,) float64` NPY vectors ordered `[x, y, z, qx, qy, qz, qw]`. Labels are versioned under `labels/<name>/v<N>.tar`.
+Large modalities are packed in tar shards. RGB and fisheye images are lossless WebP; depth and masks are PNG;
+camera poses are `(7,) float64` NPY vectors `[x, y, z, qx, qy, qz, qw]`. Labels are versioned under
+`labels/<name>/v<N>.tar`. `manifest/frames_v1.parquet` is the canonical frame inventory (133,121 records,
+with absent Ego depth, fisheye and pose marked explicitly); `frames_v2.parquet` is historical only.
+
+</details>
 
-## VQA data
+<details>
+<summary><b>Object and mask identifiers</b></summary>
+
+| Field | Scope and rule |
+|---|---|
+| `global_object_id` | canonical integer identity, unique and contiguous from 1 to 70 |
+| `source_catalog_id` | original catalog identity, stored as a string; dotted values such as `"18.2"` must stay strings |
+| `object_id` | canonical SOS folder key, for example `boot.2` |
+| `local_mask_id` / `mask_index` | local to one scene condition; join with scene, view and phase before mapping to a global ID |
+| `sample_id` | stable identifier of a VQA record; never derive object identity from it |
+
+The source of truth is `manifest/object_catalog_v1.json`. Join MOS masks through
+`manifest/mos_mask_object_map_v1.parquet` and tracking prompts through
+`tracking/metadata/text_initialization_v1/scene_condition_vocab.parquet`.
+
+</details>
+
+<details>
+<summary><b>VQA files</b></summary>
 
 | File | Rows | Purpose |
 |---|---:|---|
-| `vqa/attribute.parquet` | 3,241,564 | color, material, function, composition, count and related attribute questions |
-| `vqa/spatial_bbox.parquet` | 543,017 | spatial questions with bbox answers |
-| `vqa/spatial_binary.parquet` | 744,741 | left/right and above/below binary questions |
+| `vqa/attribute.parquet` | 3,241,564 | colour, material, function, composition, count and related attribute questions |
+| `vqa/spatial_bbox.parquet` | 543,017 | spatial questions with box answers |
+| `vqa/spatial_binary.parquet` | 744,741 | left/right and above/below questions |
 | `vqa/incontext_mos_attribute_bbox.parquet` | 317,873 | MOS two-image attribute transfer |
 | `vqa/incontext_ego_attribute_bbox.parquet` | 113,193 | Ego two-image attribute transfer |
 | `vqa/incontext_mos_spatial_bbox.parquet` | 318,017 | MOS two-image spatial reasoning |
 
-The runnable release manifest is `vqa/metadata/benchmark_subset_v1/benchmark_available_31500.jsonl`. `benchmark_pending_4500.jsonl` contains reserved descriptors, not runnable questions: Ego has no depth, so those spatial questions have not been fabricated. Published checksum and validation reports remain beside each VQA release.
+The runnable manifest is `vqa/metadata/benchmark_subset_v1/benchmark_available_31500.jsonl`.
+`benchmark_pending_4500.jsonl` holds reserved descriptors, not runnable questions: Ego has no depth, so those
+spatial questions are not fabricated. A model answers with a target-image box; the evaluator never passes
+ground truth to the model.
+
+</details>
 
-## Reproducibility and validation
+<details>
+<summary><b>Reproducibility</b></summary>
 
-- Pin the resolved commit SHA for published results.
-- Verify downloaded files with the SHA-256 manifests under `manifest/`, `vqa/metadata/`, and `tracking/metadata/`.
-- Treat `manifest/current.json` as the active metadata index.
-- Preserve `source_catalog_id` as text when reading CSV; prefer JSON or Parquet when schema fidelity matters.
-- Use `manifest/frames_v1.parquet` for all frame discovery.
+- Pin the dataset commit you used and report it with your results.
+- Verify downloads with the SHA-256 manifests under `manifest/`, `vqa/metadata/` and `tracking/metadata/`.
+- `manifest/current.json` is the active metadata index.
+- Read `source_catalog_id` as text from CSV; prefer Parquet or JSON when types matter.
 
-The consolidation preserves every file from `main`, then adds the complete VQA and tracking metadata trees from their source branches. `manifest/release_naren_all_v1.json` records source revisions, counts and the branch-level integrity audit.
+</details>
 
-## Citation and license
+## Citation
+
+```bibtex
+@misc{rpx2026,
+  title  = {Same Scene, Different Story: Evaluating Robot Perception Across Scene Phases in the Wild},
+  author = {{Jishnu Jaykumar P} and Kadosh, Itay and Vijayakumar, Narendhiran and Kamath, Srinanditha and
+            Allu, Sai Haneesh and Rangappa, Govind Tyagi and Maheshwari, Animesh and Wang, Jikai and Xiang, Yu},
+  year   = {2026},
+  note   = {Dataset: \url{https://huggingface.co/datasets/IRVLUTD/RPX}}
+}
+```
 
-If you use RPX, cite the accompanying paper. The final BibTeX entry will be added when the paper is released.
+## License
 
-The RPX dataset is released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license. You may share and adapt it with attribution. The benchmark code is distributed separately under the MIT license in the GitHub repository.
+The RPX dataset is released under [Creative Commons Attribution 4.0 (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/):
+share and adapt it with attribution. The benchmark code is MIT-licensed in the
+[GitHub repository](https://github.com/IRVLUTD/RPX).

splits/easy.csv

@@ -1,101 +1,100 @@
-scene_phase,scene_id,phase_index,phase_name,difficulty,rpx_ds,scene_tier,scene_score
-scene010.phase2,scene010,2,clean,easy,0.421333,hard,0.530692
-scene011.phase0,scene011,0,clutter,easy,0.365533,easy,0.355500
-scene011.phase1,scene011,1,interaction,easy,0.350933,easy,0.355500
-scene011.phase2,scene011,2,clean,easy,0.350033,easy,0.355500
-scene012.phase1,scene012,1,interaction,easy,0.286583,easy,0.422708
-scene012.phase2,scene012,2,clean,easy,0.345783,easy,0.422708
-scene013.phase2,scene013,2,clean,easy,0.375533,easy,0.448228
-scene014.phase2,scene014,2,clean,easy,0.416883,medium,0.503586
-scene015.phase0,scene015,0,clutter,easy,0.286733,easy,0.339533
-scene015.phase1,scene015,1,interaction,easy,0.351133,easy,0.339533
-scene015.phase2,scene015,2,clean,easy,0.380733,easy,0.339533
-scene016.phase2,scene016,2,clean,easy,0.440233,hard,0.542942
-scene017.phase1,scene017,1,interaction,easy,0.378933,medium,0.529922
-scene019.phase0,scene019,0,clutter,easy,0.434183,hard,0.557081
-scene021.phase1,scene021,1,interaction,easy,0.431925,medium,0.481681
-scene022.phase0,scene022,0,clutter,easy,0.301733,easy,0.291817
-scene022.phase1,scene022,1,interaction,easy,0.291983,easy,0.291817
-scene022.phase2,scene022,2,clean,easy,0.281733,easy,0.291817
-scene023.phase1,scene023,1,interaction,easy,0.359450,easy,0.421014
-scene023.phase2,scene023,2,clean,easy,0.423408,easy,0.421014
-scene024.phase1,scene024,1,interaction,easy,0.318133,easy,0.412094
-scene025.phase1,scene025,1,interaction,easy,0.428242,medium,0.466844
-scene025.phase2,scene025,2,clean,easy,0.443317,medium,0.466844
-scene026.phase1,scene026,1,interaction,easy,0.367933,easy,0.456700
-scene027.phase1,scene027,1,interaction,easy,0.394333,hard,0.544003
-scene028.phase1,scene028,1,interaction,easy,0.382133,medium,0.500117
-scene029.phase0,scene029,0,clutter,easy,0.435033,easy,0.413000
-scene029.phase1,scene029,1,interaction,easy,0.339533,easy,0.413000
-scene030.phase1,scene030,1,interaction,easy,0.356233,easy,0.432950
-scene031.phase0,scene031,0,clutter,easy,0.400333,easy,0.363217
-scene031.phase1,scene031,1,interaction,easy,0.332283,easy,0.363217
-scene031.phase2,scene031,2,clean,easy,0.357033,easy,0.363217
-scene032.phase0,scene032,0,clutter,easy,0.363383,easy,0.382250
-scene032.phase1,scene032,1,interaction,easy,0.423733,easy,0.382250
-scene032.phase2,scene032,2,clean,easy,0.359633,easy,0.382250
-scene034.phase0,scene034,0,clutter,easy,0.437067,easy,0.431028
-scene034.phase1,scene034,1,interaction,easy,0.335633,easy,0.431028
-scene035.phase1,scene035,1,interaction,easy,0.387975,easy,0.414164
-scene035.phase2,scene035,2,clean,easy,0.339733,easy,0.414164
-scene036.phase0,scene036,0,clutter,easy,0.427183,medium,0.492242
-scene036.phase1,scene036,1,interaction,easy,0.423883,medium,0.492242
-scene037.phase1,scene037,1,interaction,easy,0.379133,easy,0.448586
-scene037.phase2,scene037,2,clean,easy,0.429333,easy,0.448586
-scene038.phase0,scene038,0,clutter,easy,0.416433,easy,0.453461
-scene038.phase1,scene038,1,interaction,easy,0.379833,easy,0.453461
-scene004.phase0,scene004,0,clutter,easy,0.433933,easy,0.459281
-scene004.phase2,scene004,2,clean,easy,0.392333,easy,0.459281
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splits/easy.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6c9e98f9335623e323fdebfcce1b62602c059e037474494ab74413e65ad3618a
-size 7564
+oid sha256:a63f5aa65f7db66f54770d978267f09ed447b9cd3e42655918fb332bb8b3663b
+size 7789

splits/easy.txt

@@ -1,32 +1,39 @@
-scene010.phase2
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@@ -35,66 +42,58 @@ scene032.phase1
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 scene034.phase0
 scene034.phase1
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splits/hard.csv

@@ -1,101 +1,103 @@
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splits/hard.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:78a5a34fe49ad71479f3d4f2297794bad3135bdd492bc5945d95d4b69673b657
-size 7483
+oid sha256:e3ef2bf1c4a80ad9ae0d2f309703c0959acbde399190e890d15fd5d0386595a6
+size 7844

splits/hard.txt

@@ -1,56 +1,63 @@
 scene001.phase0
 scene001.phase1
 scene001.phase2
+scene002.phase0
+scene002.phase1
+scene002.phase2
+scene003.phase0
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 scene010.phase0
 scene010.phase1
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 scene016.phase0
 scene016.phase1
-scene017.phase0
-scene017.phase2
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 scene018.phase0
 scene018.phase1
 scene018.phase2
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 scene019.phase1
 scene019.phase2
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-scene002.phase1
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 scene020.phase0
 scene020.phase1
 scene020.phase2
 scene027.phase0
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 scene027.phase2
-scene028.phase0
-scene003.phase1
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 scene033.phase0
-scene036.phase2
-scene038.phase2
+scene033.phase1
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 scene039.phase0
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 scene039.phase2
 scene040.phase0
 scene040.phase1
 scene040.phase2
 scene041.phase0
 scene041.phase1
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 scene042.phase0
 scene042.phase1
 scene042.phase2
 scene043.phase0
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 scene043.phase2
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 scene044.phase2
 scene045.phase0
 scene045.phase1
 scene045.phase2
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 scene046.phase1
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 scene049.phase2
-scene005.phase0
-scene005.phase1
-scene005.phase2
-scene058.phase0
 scene061.phase0
 scene061.phase1
 scene061.phase2
@@ -61,13 +68,10 @@ scene063.phase0
 scene063.phase1
 scene063.phase2
 scene067.phase0
+scene067.phase1
 scene067.phase2
-scene007.phase0
-scene070.phase1
-scene070.phase2
-scene076.phase0
-scene079.phase2
 scene080.phase0
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 scene080.phase2
 scene081.phase0
 scene081.phase1
@@ -77,24 +81,22 @@ scene082.phase1
 scene082.phase2
 scene083.phase0
 scene083.phase1
-scene084.phase1
+scene083.phase2
 scene085.phase0
 scene085.phase1
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 scene087.phase0
 scene087.phase1
 scene087.phase2
 scene088.phase0
 scene088.phase1
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 scene095.phase0
+scene095.phase1
 scene095.phase2
 scene096.phase0
 scene096.phase1
 scene096.phase2
 scene098.phase0
+scene098.phase1
+scene098.phase2

splits/medium.csv

@@ -1,101 +1,100 @@
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splits/medium.parquet

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 version https://git-lfs.github.com/spec/v1
-oid sha256:f0fac96d73109a94cb1a4fbf5a284b823728e6aeed755b9246ed5d5590eff167
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splits/medium.txt

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