Add files using upload-large-folder tool
Browse files- README.md +24 -5
- manifest/checksums.json +0 -0
- manifest/current.json +9 -0
- rpx_croissant.json +7 -7
README.md
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@@ -129,13 +129,32 @@ in this cleaned release. Use `manifest/frames_v1.parquet` and the identity
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manifests under `manifest/` as the source of truth for the v1 MOS/SOS release;
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use `manifest/frames_v2.parquet` when optional ego rows are present.
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| modality | files | bytes |
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|---|---:|---:|
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| `cam_pose` | 110,000 |
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| `depth` | 110,000 |
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| `fisheye` | 220,000 |
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| `rgb` | 110,000 |
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| `masks` | 110,000 |
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## Quick start
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manifests under `manifest/` as the source of truth for the v1 MOS/SOS release;
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use `manifest/frames_v2.parquet` when optional ego rows are present.
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This release applies a storage-only, lossless re-encoding to the published
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MOS/SOS data. Every transformed file was decoded immediately after writing and
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compared with its source array/value:
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* `rgb/` and `fisheye/` PNGs are stored as lossless WebP.
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* `depth/` and `sam2/masks/` remain PNG, re-encoded at compression level 9;
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their 16-bit modes and pixel values are unchanged.
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* `cam_pose/` changes from per-frame `.npz` wrappers to per-frame `.npy`
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`(7,) float64` vectors ordered as `[x, y, z, qx, qy, qz, qw]`.
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* `masks_aux` and `sam2_meta` are unchanged and retain the exact tar hashes
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from the previous release.
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Across 1,451,790 packed members, logical storage falls from **248.5 GB** to
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**213.0 GB** (35.5 GB / 14.3% smaller) without changing decoded data. Optional
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ego metadata remains available, but no unpublished ego tar is added by this
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update.
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| modality | files | bytes |
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|---|---:|---:|
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| `cam_pose` | 110,000 | 20.2 MB |
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| `depth` | 110,000 | 13.2 GB |
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| `fisheye` | 220,000 | 56.9 GB |
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| `rgb` | 110,000 | 24.3 GB |
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| `masks` | 110,000 | 212.3 MB |
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| `masks_aux` | 790,631 | 118.4 GB |
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| `sam2_meta` | 1,159 | 535.6 kB |
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## Quick start
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manifest/checksums.json
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The diff for this file is too large to render.
See raw diff
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manifest/current.json
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@@ -78,5 +78,14 @@
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"mos_mask_object_map": "v1",
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"ego_frames": "v1",
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"mos_ego_mask_object_map": "v1"
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}
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}
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"mos_mask_object_map": "v1",
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"ego_frames": "v1",
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"mos_ego_mask_object_map": "v1"
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},
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"modality_extensions": {
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"rgb": ".webp",
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"depth": ".png",
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"fisheye": ".webp",
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"fisheye_left": ".webp",
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"fisheye_right": ".webp",
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"masks": ".png",
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"cam_pose": ".npy"
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}
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}
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rpx_croissant.json
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@@ -44,12 +44,12 @@
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},
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"@type": "sc:Dataset",
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"name": "RPX",
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"description": "RPX (Robot Perception X) is a unified real-world RGB-D benchmark for evaluating robot perception under embodied deployment conditions. It comprises
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"conformsTo": "http://mlcommons.org/croissant/1.0",
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"citeAs": "TODO: add bibtex citation after paper is published",
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"license": "https://creativecommons.org/licenses/by/4.0/",
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"url": "https://huggingface.co/datasets/IRVLUTD/RPX",
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"version": "1.
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"isLiveDataset": false,
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"creator": {
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"@type": "Organization",
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"@type": "cr:FileObject",
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"@id": "rpx-full",
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"name": "rpx-full",
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"description": "Full
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"contentUrl": "https://anonymous.4open.science/r/RPX/full",
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"encodingFormat": "application/zip",
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"sha256": "TODO"
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"@type": "cr:Field",
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"@id": "frames/rgb_path",
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"name": "rgb_path",
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"description": "Relative path to RGB image (
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"dataType": "sc:Text"
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},
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{
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"@type": "cr:Field",
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"@id": "frames/depth_path",
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"name": "depth_path",
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"description": "Relative path to depth map (
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"dataType": "sc:Text"
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},
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{
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"@type": "cr:Field",
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"@id": "frames/camera_pose",
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"name": "camera_pose",
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"description": "
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"dataType": "sc:Text"
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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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"@type": "sc:Dataset",
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"name": "RPX",
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"description": "RPX (Robot Perception X) is a unified real-world RGB-D benchmark for evaluating robot perception under embodied deployment conditions. It comprises 110,000 released MOS/SOS frames across 100 indoor multi-object scenes and 70 selected single-object captures, using Intel D435 and T265 sensors. Ground-truth annotations include instance masks, tracklets, metric depth, 6-DoF camera pose, and language attributes, enabling evaluation of perception tasks on identical scenes and objects.\n\nNote on on-disk shape: per-frame records are physically packed into per-(scene, phase, modality) tar shards on the HF repo (scenes/<scene>/<phase>/<modality>.tar for raw modalities; labels/<modality>/v<N>.tar for versioned labels). RGB and fisheye frames use lossless WebP; depth and masks use lossless PNG; camera poses use per-frame NPY float64 vectors. Every transformed file was round-trip verified against its source. The rpx_benchmark.dataset_hub downloader resolves selective task+split downloads from these shards transparently.",
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"conformsTo": "http://mlcommons.org/croissant/1.0",
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"citeAs": "TODO: add bibtex citation after paper is published",
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"license": "https://creativecommons.org/licenses/by/4.0/",
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"url": "https://huggingface.co/datasets/IRVLUTD/RPX",
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"version": "1.1.0",
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"isLiveDataset": false,
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"creator": {
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"@type": "Organization",
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"@type": "cr:FileObject",
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"@id": "rpx-full",
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"name": "rpx-full",
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"description": "Full 110,000-frame MOS/SOS dataset with all annotations",
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"contentUrl": "https://anonymous.4open.science/r/RPX/full",
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"encodingFormat": "application/zip",
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"sha256": "TODO"
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"@type": "cr:Field",
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"@id": "frames/rgb_path",
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"name": "rgb_path",
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"description": "Relative path to RGB image (lossless WebP, RGB uint8)",
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"dataType": "sc:Text"
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},
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{
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"@type": "cr:Field",
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"@id": "frames/depth_path",
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"name": "depth_path",
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"description": "Relative path to depth map (lossless PNG, uint16 millimetres)",
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"dataType": "sc:Text"
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},
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{
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"@type": "cr:Field",
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"@id": "frames/camera_pose",
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"name": "camera_pose",
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"description": "Relative path to a (7,) float64 NPY pose vector [x, y, z, qx, qy, qz, qw] from T265",
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"dataType": "sc:Text"
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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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