Upload 138 files

README.md

@@ -1,5 +1,149 @@
 ---
-license: other
-license_name: scannet-terms-of-use
-license_link: LICENSE
+pretty_name: Metric-Bench Test
+language:
+- en
+task_categories:
+- visual-question-answering
+tags:
+- benchmark
+- spatial-reasoning
+- metric-measurement
+- scannet
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: test.parquet
 ---
+
+# Metric-Bench Test
+
+Metric-Bench Test set evaluates metric spatial understanding from indoor RGB images and explicit anchor measurements. Each example asks for a physical measurement of a referred object or the distance between two objects. Inputs consist of an image and an English question; the reference answer is a numeric JSON object.
+
+This release contains **1,340 questions, 134 images and 20 scenes**, with one `test` split. It is a reconstructed test revision and does not reproduce or re-evaluate previously reported paper scores. No training or validation split is included.
+
+## Dataset composition
+
+Each image has ten questions. Questions provide either two anchors (539 examples) or three anchors (801 examples).
+
+| Question type | Examples | Answer value |
+| --- | ---: | --- |
+| width | 268 | Number |
+| length | 134 | Number |
+| height | 134 | Number |
+| dimension | 134 | `[width, height, length]` |
+| center | 134 | `[x, y, z]` |
+| depth | 134 | Number |
+| elevation | 134 | Number |
+| distance | 134 | Number |
+| box | 134 | `[x, y, z, width, height, length]` |
+
+## Repository structure
+
+```text
+README.md
+test.parquet
+dataset_stats.json
+SHA256SUMS
+images/
+  scene0084_00/
+    1130.jpg
+  ...
+```
+
+`test.parquet` is the canonical evaluation table. Image paths are relative to the repository root. Images are stored separately as JPEG files; the table contains path strings, not embedded image bytes or automatically decoded Hugging Face `Image` features. `dataset_stats.json` contains aggregate counts and packaging checks. `SHA256SUMS` covers all other release files.
+
+## Data fields
+
+| Field | Type | Description |
+| --- | --- | --- |
+| `id` | string | Unique example identifier, preserved from the source export. |
+| `images` | list of strings | One repository-relative image path per example. |
+| `question` | string | Complete question, including the supplied anchor measurements. |
+| `question_type` | string | Measurement type and expected answer key. |
+| `ground_truth` | string | JSON-encoded object with one measurement key; parse with `json.loads`. |
+| `scene_id` | string | Source ScanNet scene identifier. |
+| `frame_id` | string | Source RGB frame identifier. |
+| `anchor_count` | int64 | Number of supplied anchors: two or three. |
+
+Ground truth uses a JSON string so scalar and vector answers share one stable column type. For example, `{"length":0.388}` and `{"center":[0.037,-0.633,2.712]}` are valid reference answers. Numeric precision is preserved from the source export.
+
+## Loading
+
+Install `datasets`, `huggingface_hub` and `Pillow`. From a downloaded repository, run:
+
+```python
+import json
+from pathlib import Path
+from datasets import load_dataset
+from PIL import Image
+
+root = Path(".")  # Repository root
+test = load_dataset(
+    "parquet", data_files={"test": str(root / "test.parquet")}, split="test"
+)
+example = test[0]
+with Image.open(root / example["images"][0]) as image:
+    image = image.convert("RGB")
+question = example["question"]
+reference = json.loads(example["ground_truth"])
+```
+
+After publication, download the table and resolve each image from the same repository revision:
+
+```python
+import json
+from datasets import load_dataset
+from huggingface_hub import HfApi, hf_hub_download
+from PIL import Image
+
+repo_id = input("Hugging Face dataset repository ID: ").strip()
+revision = HfApi().dataset_info(repo_id).sha
+test = load_dataset(repo_id, revision=revision, split="test")
+example = test[0]
+image_path = hf_hub_download(
+    repo_id=repo_id, repo_type="dataset", revision=revision,
+    filename=example["images"][0],
+)
+with Image.open(image_path) as image:
+    image = image.convert("RGB")
+reference = json.loads(example["ground_truth"])
+```
+
+The explicit split configuration follows the [Hugging Face dataset configuration documentation](https://huggingface.co/docs/hub/datasets-manual-configuration). Loading the table alone does not download the separate JPEG files.
+
+## Measurement conventions
+
+All measurements are in **meters**.
+
+- `width`, `height` and `length` are physical object size parameters; `dimension` orders them as `[width, height, length]`.
+- `center` is `[x, y, z]` in camera coordinates. `depth` is its camera-Z component.
+- `box` concatenates camera center and physical dimensions as `[x, y, z, width, height, length]`. The size parameters are not camera-axis-aligned bounding-box extents, and this representation does not include object rotation.
+- `elevation` is the object's bottom elevation in the source world coordinate system: `world_center_z - world_height / 2`. It is not necessarily height above the local floor.
+- `distance` is the nearest-surface distance between the two source world-axis-aligned bounding boxes, not the distance between object centers or reconstructed meshes.
+
+## Evaluation use
+
+Provide only the image and `question` as model input. Ask for one JSON object with the key specified by `question_type` and a numeric scalar or array in the order above. For example: `Return only a JSON object with key "length" and a numeric value in meters.` No explanation or intermediate reasoning is required. Keep `ground_truth` outside the model input and use it only for scoring.
+
+This release provides reference answers but does not ship an official scoring implementation or specify a paper-equivalent acceptance threshold. For reproducible evaluations, report the repository revision, model version, prompt, decoding settings, invalid-output handling and numeric tolerance or error metric. Report results by question type; scalar and vector outputs require explicit scoring definitions. State whether aggregate results are weighted by example count or averaged across question types, because width has twice as many examples as each other type.
+
+The intended use is held-out evaluation of visual metric estimation with supplied anchors. Training or tuning on the released answers compromises this use. Multiple questions share each image and scene, so question-level random partitions are not independent scene splits.
+
+## Source and preparation
+
+RGB images and underlying scene annotations originate from [ScanNet](https://github.com/ScanNet/ScanNet). This release preserves the source export's question text, reference answers, identifiers and JPEG bytes. Packaging removes internal curation metadata, machine-specific paths, auxiliary target geometry and conversation instructions requesting intermediate reasoning.
+
+Source curation included geometry checks and automated visual/text review; it does not establish human verification of every example. The release packaging checks cover record uniqueness, answer preservation, Parquet round-trip integrity, relative image paths, image decoding and source image hashes. They do not constitute a fresh independent geometric or semantic audit.
+
+## Limitations
+
+The benchmark covers a small set of indoor scenes and supplied anchor measurements. Performance should not be generalized to outdoor scenes, unseen domains or unassisted metric estimation. Referring expressions, occlusion and source geometry may introduce ambiguity or annotation errors. Axis-aligned source boxes approximate object surfaces, which affects the interpretation of distance. This package alone does not establish disjointness from any external training corpus.
+
+## Data terms and attribution
+
+ScanNet data is governed by the [ScanNet Terms of Use](https://kaldir.vc.in.tum.de/scannet/ScanNet_TOS.pdf), as stated in the [official ScanNet repository](https://github.com/ScanNet/ScanNet). This packaging does not grant additional rights to the source images or annotations. Public image redistribution must be covered by the publisher's applicable permissions. No separate license for the benchmark-authored questions is supplied in the source package; this card does not assign one on the authors' behalf.
+
+When reporting results, identify this release as **Metric-Bench Test** and include its repository revision. Credit the underlying ScanNet dataset using the citation provided in the official ScanNet repository. No benchmark paper identifier or author list is supplied with this package, so a benchmark bibliographic citation is not fabricated here.

SHA256SUMS

@@ -0,0 +1,137 @@
+b7b6acd854c1f45574c761ee24c7276b60863cae593866ca0cee386d95538970  README.md
+7c9ebc2185c0653700342c508df482502316ecb6924da441d85eca24b203bcf1  dataset_stats.json
+6e17a0190dba233c98c341c0de07cc611c02b774f7b677495992e0fd814b3297  images/scene0025_00/1210.jpg
+ec457735f75a82a503bafc028209b4b5e3bc8f30a06b156147fd1ede85b4596e  images/scene0025_00/1220.jpg
+ea087691fa2344249265137798f89861d9e3a91b313f0c7d1853e75c0350f9ef  images/scene0025_00/1230.jpg
+471479fef5c975380c5849ffc39960fb9af1e764c06ff02073e5c726d79a69ed  images/scene0050_00/290.jpg
+0e7fcb6d9d93ad56db04f39a13446505cca0badf457838d2601417e7b2b6c98e  images/scene0050_00/300.jpg
+114a36923f73acab3c8b501eec2c208e38441dde7bd7b024c32de19d54825ae4  images/scene0050_00/330.jpg
+ab9b51838a8eb49fdf41e29a917f90336abac3372a2591c107b1299fce0c283d  images/scene0050_00/340.jpg
+8e65c56e604fcfc6969fbe587aa27ee8b0033bbaf9a37c061e3b2426c01ba5d9  images/scene0050_00/3760.jpg
+e9328ea9ec7e3ca7080c83ea2bd4f2931e9da1df6629976935b4ed35fa77d72e  images/scene0084_00/1130.jpg
+83e158de51085dcaa9c539b0d2ce05b86818d938690b4e9c21446a4043ff51d8  images/scene0084_00/1150.jpg
+79505e34290e4ecd2cab01f1eba89a424b6d06cfb317432546c4f57c3fe8d478  images/scene0084_00/1170.jpg
+9bf9dcb86a1eeebb81cc16ab0e1f5fd50e26b24906ba41bb688e34097c5448ea  images/scene0084_00/1190.jpg
+6aeafd110e657075e90bc79763bdd47afbcd19a3da22c6e667adad15e03e92ba  images/scene0084_00/1220.jpg
+56ef26cff9f5d52ed0cb8c7960e009a1897971211d9e7ce838481b83cd95448e  images/scene0084_00/1320.jpg
+bd762a72ef006e9c0dcee3391b5a166f5c63787b83d3cfcccbaee18511ba9166  images/scene0084_00/1350.jpg
+187df105af827f8416c491bfe0c5c7268fac6fd14571b262edc1653c8ed28854  images/scene0084_00/1380.jpg
+dfec8af3225f0c5a5eca24cf1fe523442e471d1f716c63f81ed899b7ed614dff  images/scene0084_00/1400.jpg
+29986fafd461f2944d6a25047288b0768d774077eef3beadd3894c14f1ad4879  images/scene0084_00/1440.jpg
+1fb874264c3c790fabd415a9c6f32f0e529ab79cb59802d4fdc8aa2c3d5a7369  images/scene0084_00/1950.jpg
+18594985261d15b93a5e00d1269b81d71f8e4d84ab55dda6a17db70a998996de  images/scene0084_00/1970.jpg
+93a348e27ec4bcf75890a57ec386233fbfef405be99df6665b111f0ea9b75d50  images/scene0086_00/100.jpg
+5fe7a5804bcf9a98d8123a7be8506057119ae85a4fd37d0e1317414e88a86667  images/scene0086_00/1020.jpg
+ae0428985512a58d48f73e736df19f1e0b51120afa51402482982d8e61029a48  images/scene0086_00/1070.jpg
+dd44a719b7ce6b752b7e9b2bdfff665e5b78ad64759b7f9c9a792b709ccb734b  images/scene0086_00/1100.jpg
+a41a15b25dd26d766c021fa96f07a33f25bc3a3aec3f685a831e0acd4e6af412  images/scene0086_00/1140.jpg
+927fd3275f388ec0835becb505e075f4383d21cbc373607161e145e907b6f003  images/scene0086_00/120.jpg
+09cd90ff191d4427f3733cd1c3bed22bc49b0bca291160b60ee2a98a46d049fe  images/scene0086_00/150.jpg
+80347f6f114af7f52c0430736c41f5ac39a9fa19f0f92eae4f50db7c234ae00b  images/scene0086_00/580.jpg
+19e5727e92f8b0a226f7aac3ab4ff259065aa62d6fa9c8866aaacc2c45c4dcbd  images/scene0086_00/60.jpg
+49342f330ea089c9d258046126c7a667502e11444e7f7e1763cf61c5eb6f4e8c  images/scene0086_00/600.jpg
+8d806fa178025e7ca7fd9fba91f09f82e211adb1ba8470f08fe5be189dabc18d  images/scene0086_00/640.jpg
+ae23aaccdcb3d1584a25f619a9491a3dc5b8100f5dfe6496ca31189bb4aa0da1  images/scene0086_00/80.jpg
+a978f01e537eef38b2d1600bb06994523cee90c0e73112b183bfdcfea1c545aa  images/scene0146_00/10.jpg
+85621f411f8d0b34b28a0ea13adb7ed6620e60887369d110dc38891db04ce8cc  images/scene0146_00/1350.jpg
+3ea8f3f9d84b9144c906d7fa87556ebecd33f0fcdb076e7ea32f8ceb0434f99a  images/scene0146_00/1360.jpg
+385300d1ffdb1776da0a39fe19ef085f4004abb9bcbabe9906059d8223b25cb0  images/scene0146_00/1370.jpg
+8c98be56d0a08a6389fb49808fab8d80ec46ce3ae90a927ce2c052912e3aaed7  images/scene0146_00/1380.jpg
+9f422ba49b8025358dcb0eab08d21b6e88ce7e0b2a17ccc737fad6662b34477c  images/scene0146_00/1390.jpg
+d5c60033258ac61714259ef06bc80ddf80b7a1688d4687007e26c582d4f88d2d  images/scene0146_00/1400.jpg
+93833107cbecad25ebbbba8b53b56bac4ad1eae9ac8e99d1557162fb6d0f2310  images/scene0146_00/20.jpg
+2d9f0a4a0614091d6a3e02755ff2678bd554f0eb257a97a5a7ae804f814a2989  images/scene0146_00/30.jpg
+8d086908f0ea3c292d155751a41fb092b80af61b44186067d9b1905ad837dd08  images/scene0146_00/40.jpg
+747ad81ef7f29c003500b60847a14418a204c5b7acc7b345dcd9d25e4d78eafa  images/scene0146_00/80.jpg
+8252762bc1b2c3a32913cf885461c8b0f2bffb2394982215f3cd3363abfe9c66  images/scene0164_00/120.jpg
+7a24683a7ddd9595785904ad6501b507c592e7ad1785b00905356120abfaaefc  images/scene0164_00/1200.jpg
+087e4a7a3be07b3db801a6f860f4b075c08831e45daf6ff16ae6b23be64bc7f6  images/scene0164_00/1250.jpg
+61d0222b56ab31423bfc83c3dba7ecde865cbb090c3a3210ebeae58ddcf88b4b  images/scene0164_00/1310.jpg
+9227fd3dd14268a56da16b5ed4352faa7bda6e3947a47e210065d8c541af90d4  images/scene0164_00/150.jpg
+c465ba3f6493119a6ad49b2a99838695b1271338a0145b3c332f368d54859cfc  images/scene0164_00/170.jpg
+ae47e9daf576730e711f7e27b25fa8a78ce2e0b65ae89fe917990891f3b94232  images/scene0164_00/1880.jpg
+b197b1b39f5b3a63571cbc0b17610cc1173adb25e22bdd9649bf63e12fd6c317  images/scene0164_00/190.jpg
+f93de78be58dbac43191c2777fd70664c0bebe8daa547796d0551a377de6aa4d  images/scene0164_00/50.jpg
+5496c923b3ce860b470ac6675f37b1356d732974e3c1dcae747049c6081e7a7c  images/scene0164_00/690.jpg
+efd278815c9c75c6d998ba5434f7fe09e6b2cee9f181eebe1c83e627ab09885f  images/scene0164_00/80.jpg
+03dd7f11e4a70f464935c34edf100f3feb7a6b141e3d7eda0287e7fffe46ffdf  images/scene0164_00/810.jpg
+268617b1f4037060292bc28a9116b30157954e8e83a4eb3d3e40cc83b9a2151b  images/scene0207_00/1040.jpg
+b99e766ed81e1574d2beedd86774a544bc993d3a52c13b7773f5cc597a4b3c3f  images/scene0207_00/240.jpg
+0f0dd0d67fca4e44a5f3346d678449269dc8ee49c9fe5d1661df5a8fbf4ae29c  images/scene0207_00/260.jpg
+36c3efe7744a917f42baf20317c878256ce938f2b3d89f4ac0d431a801fc0dbb  images/scene0207_00/270.jpg
+3ded54b06eaf3d73b4bde706eb31cae203c1f97e8b944d43a8f7806d5571d009  images/scene0207_00/50.jpg
+1976bf47cc63fb50da47333208496483eec5a6cce9f48254811a71cd4e889afb  images/scene0207_00/60.jpg
+d8391970dc685f52fd93aa28d3821c35adbf198c389a2b7c2551b1b9c1fa1c54  images/scene0207_00/980.jpg
+5230e196120b69daf10064a3b20a60c2ed5c634d58342a794b080a64c7ef33cc  images/scene0357_00/0.jpg
+3c610df8b8c4386a5caa54f1b47c9168788e2b4bb115ecd8c2a894a5cb3a5b21  images/scene0357_00/20.jpg
+91f22c62f70f504977d623e591a334844bd8311f384f6c0f411b900e1f18e65b  images/scene0357_00/50.jpg
+3d53406aef35e5f5584379c007f24ed8e3cbc86faa4893f7e553e2afda78ddc7  images/scene0357_00/70.jpg
+1a9370f87fd75b6503eb2af8e547bd9a200c0f6e633e273e5e58581631a5e2d6  images/scene0357_00/710.jpg
+78c34b807b47f420bd83cbf3058761d1157f8a4f929ed75e3e6da2a3ac5918e3  images/scene0357_00/80.jpg
+ca756b7c220f45973c164d2f6259cb24a34ac8e8240e16e0e3a1a5ab868dd7d7  images/scene0357_00/890.jpg
+b21d4fe75ccaa5bd8e071f0103b59b96bc1f7a0e3004ce9a5e68c98844064739  images/scene0378_00/1350.jpg
+4ab61816a1ea8f193f1024457ddeb218e077a551dd905204f66fe98d46b076e2  images/scene0378_00/1370.jpg
+e894716c6700617cd018ad620a4eb2574f11d6d19d1fb9981faf47af70a2ce78  images/scene0378_00/1460.jpg
+b5b7e9003a0afd41e9cf31bd5da47c45c27982adaba5244f0b3ec63fe1ad716f  images/scene0378_00/1480.jpg
+410ab8d8c92e4306315cbbfef6dfd3840c5bd7c446a6105dfdcd49fbe5986b73  images/scene0378_00/1610.jpg
+40c4f21b4dacdc74cfbb6fa0a262dfed6deebe5ed800f33731e0b31a8b48d142  images/scene0435_00/2200.jpg
+b17ffe4a036d695a1bb9d70facc8f9312bf3c649e9b0ab8832cced2004b97404  images/scene0435_00/2480.jpg
+83e9f789dbdaee8b42903174eb1c1dfedb3731d13168de859ece5ec30788e569  images/scene0435_00/2760.jpg
+39338e69581614d5999b469fb09aafa67e68fc95c6b42e7d1ca0de04d941fc5d  images/scene0474_00/1200.jpg
+f95d478251beae9ea4f8d7ca4b9e5a84b6da2665f745570afb72d43dcd1bb6f9  images/scene0474_00/1400.jpg
+080776497d98e1c99521456870c8ec7628fa39358fa0b5da2ab2361df4ef1eb5  images/scene0474_00/1550.jpg
+29641376a21a0ed232f280e1e904e0868cd6be05a30e120f81f6cefd457183d9  images/scene0474_00/1560.jpg
+f4e46296ca76ba3b3d8448460b5135e7f1cc1e6301d4d132313899875dadf898  images/scene0474_00/1580.jpg
+a9c0a0096f309ce52698945f44d2c4364c193b24953486c084dcade28dbc5223  images/scene0518_00/100.jpg
+3dc21ddd477c19e569a9f972b6fbcb7579dcd5acbc8e1b1481239874453463c1  images/scene0518_00/110.jpg
+a05076864a865aa90dbc1eb17cc0d12a7b211928f274f8f0c01b8ecddfe6f0c5  images/scene0518_00/140.jpg
+2e8ba53496f0bb52fdd661203cdab999c11d5d7e0d9315e77c93ed69e1234930  images/scene0518_00/160.jpg
+30f974fe36a2d6c3b4b2f102b5ccdb8cd4561a73530baa84a7e78dec728e0126  images/scene0518_00/60.jpg
+7ce1bb7845889f9f6dbe91a609ef4777318ffb0d80329c436ec3a64855865262  images/scene0518_00/90.jpg
+f90501d4a6b5ed89e5cb555d0d3ad4d31ed988f42560f670d54f3dff340d5340  images/scene0565_00/130.jpg
+18fb66b1451d1f5fc536a61e0a4110b25f3be903d188ce9e666571ff96d4da21  images/scene0565_00/140.jpg
+4e0441e0baed5b3ba8253520fbedb26ff836ee9fe6eb7e4987ea0f37ddd70783  images/scene0565_00/150.jpg
+dc5ab84ffeecb6262d8b5b310d9a63df2b3e909ec290fe218d1478a927b2ac81  images/scene0565_00/30.jpg
+1591d304f2bd01f50c4b50315c104e64f528e630b1d65aff78764c0e38352152  images/scene0565_00/40.jpg
+9f4ef6526d976950b6c9e17d55cdf7257bdd18b804849326617f0ba00846d927  images/scene0565_00/50.jpg
+a9a737f69ba75bf6f62dad1a9dd0d9c0e613ec7a37175996f63388e2f7077368  images/scene0607_00/750.jpg
+12253cde1ea69457e1e0164032862ec6609babcf440f06503cbf48dd630ef954  images/scene0607_00/760.jpg
+8fa5048ee63cf1822d31520a67519008c13239aba22872034e7524c2999bfc09  images/scene0607_00/770.jpg
+bb00fe2bc6e704f1c9440a4f9d7719f50e04e92c1877a63052cfe1c641718c4f  images/scene0633_00/0.jpg
+02b041a7269f1962569bffaffa26a732a42668af289664b9bd69e42f355f886d  images/scene0633_00/80.jpg
+4a02d4246f8669cd0b73d06972938771e18ca6ed13f81554bee86e2b7b5afd62  images/scene0645_00/1040.jpg
+facc258aefc60f37bc9440b8f2d2c6b414a1dcbac002f1a57a223fc0465cf14b  images/scene0645_00/2660.jpg
+a01a26522f49b1f3db14b6396b36feedf50d91d118e122bc068e35b0298510e5  images/scene0645_00/2690.jpg
+a2a181379dd676c60345b4490aa87414660f1219ce39e53b625895b422f7c9db  images/scene0645_00/2730.jpg
+8649b388d5a87ade5073a94daca970ceb05996aff070c694c95fb31cda668246  images/scene0645_00/2840.jpg
+9d340ecd41c561f8a0fbefbf13fe88144ce5f2798a6262daae9874fcadcbac02  images/scene0645_00/4400.jpg
+64ec688826165a995efd6668e351b27792333a1fd8f6c8fce35ca7eaf606a564  images/scene0645_00/720.jpg
+72b99709a6423dba0693db5f7d269c5d37f08aa3abc374f62b4463b4e72c653a  images/scene0645_00/750.jpg
+c07eb7beaa8bfa7172edf2f728d179acbaa364f191ef887295feccaae68ba47b  images/scene0645_00/770.jpg
+9dfd468bdd1534d4754ca6f2d96fbaa6503bc3231e12bcf65598e1cc09ce9542  images/scene0645_00/800.jpg
+b87dda08dad886d478e7917b7d95ebdd746ab460f41e3ced1262890ed90f7ba4  images/scene0645_00/980.jpg
+30cf43ff434450a5a27321bf3adcb6fef02a6f1423f3101e45ad78390bb7b5e9  images/scene0647_00/350.jpg
+eda24f59d181b479628e768eb412a843f3090c5ed14f86c784464fb2b902f530  images/scene0647_00/360.jpg
+e92291f0154d5820fbc743918eb9c2c5b58add233acfdf2e24cbf43531c1b418  images/scene0647_00/380.jpg
+862c21cd683719eff7fef5ccb2f04b05a5efc3f053f75912e08a0e07c977360c  images/scene0647_00/390.jpg
+00eb455ffa813c809643ca9d14a55c1dc999cb760c8120dbb973cd587a0b8794  images/scene0647_00/410.jpg
+60692c71006d2281514153f49fa28c31725a95b69c989846b4402cdf01b64706  images/scene0647_00/460.jpg
+7b5e6f4619f6f44aa68fa5e2ff7b6be2ef277a99ebd632e9b037c6c281ffcef9  images/scene0647_00/660.jpg
+81d7fde7549664aef8f4116bff8def4849a7e91842ff4f11fada016341559aa1  images/scene0647_00/670.jpg
+f4feb7bf75a482746da1d3e9e64f207381941726ad01b67c63eed6262adaa673  images/scene0647_00/680.jpg
+830557f6d567137b0f318ed37e7fba13f8e9e91b779f9d86b80deb9a35ad16be  images/scene0652_00/110.jpg
+be15ae12e8fcb079271e49c8e12e0b299ef653544a717329ba261a11fdf46890  images/scene0652_00/170.jpg
+5587587c74f468ad89e3dbbd18b5e50a350db7032f8d4991e06a389e081117c8  images/scene0652_00/180.jpg
+e51c7295ac59606aa281f8ebe779dc16066a5c5afef2865e89b9add6854816c9  images/scene0652_00/190.jpg
+3ab481ee4c4642fae0cc644f158105681a7140c150d55f0bdbd12a6c334dc17e  images/scene0652_00/200.jpg
+5fba9e51b5439220f55a27e2c5a87d152ce91894db2ca56e5e4dec00efa3b138  images/scene0652_00/60.jpg
+be553b7c72bab32bd97ed31eaf7841556ef5c0bdeef3b6eb46ab8381f8562f73  images/scene0652_00/730.jpg
+d25ee3c9e5127f5da0bf82c6911c1745ccdfa42efd074b3adf01b8517f9f9879  images/scene0670_00/480.jpg
+046e51a9963f8d53d3e5e11c7a87234772576a869af1fa64f2c52bf80726e3bc  images/scene0670_00/600.jpg
+4ef74f8b76aedc2ad5a4a4c81eb9ecf5f97f17417e4c50633dbdccbfe67ac697  images/scene0670_00/640.jpg
+af48c0b63735312afe04ce4b38a7e38a944ade1d12bb0d3ecb3794ce95e5b991  images/scene0697_00/1220.jpg
+5a319dd9d9a6c18c75d740eb4c07c44da704d46ece0185f2f6dea06b099e2d7d  images/scene0697_00/1230.jpg
+724513bad15fb84c536f4471398ac469d202a41797e9b453b67cedb1ea4eeca0  images/scene0697_00/1240.jpg
+d872db2e83d3023eb72ee442ff4753fca25f90f87c91bb11dd9df4abadbe2478  images/scene0697_00/1290.jpg
+e4a86a9156e164541256f48948411fdd25c526deafdd9b26d285b03a8cac260b  images/scene0697_00/1310.jpg
+55937e7d629d69951f447713cda8d970e608734e5e2ce7b8a2c367161e4d66fb  test.parquet

dataset_stats.json

@@ -0,0 +1,31 @@
+{
+  "split": "test",
+  "num_examples": 1340,
+  "num_images": 134,
+  "num_scenes": 20,
+  "question_types": {
+    "box": 134,
+    "center": 134,
+    "depth": 134,
+    "dimension": 134,
+    "distance": 134,
+    "elevation": 134,
+    "height": 134,
+    "length": 134,
+    "width": 268
+  },
+  "anchor_counts": {
+    "2": 539,
+    "3": 801
+  },
+  "checks": {
+    "parquet_roundtrip": true,
+    "unique_ids": true,
+    "questions_and_ground_truth_preserved": true,
+    "all_relative_image_paths_resolve": true,
+    "image_hashes_match_source": true,
+    "all_images_decode": true,
+    "no_exif_metadata": true,
+    "public_field_allowlist": true
+  }
+}