Release MindView data with verified assets and concise guides

Complete dataset indices, deterministic asset archives, integrity manifests, and usage instructions.

.gitattributes

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-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+* text=auto eol=lf
+*.jsonl filter=lfs diff=lfs merge=lfs -text
 *.tar filter=lfs diff=lfs merge=lfs -text
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+*.pdf -text

DATA_LICENSE.md

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+# Data sources and terms
+
+MindView combines original task annotations with RGB images and depth maps from ScanNet, ScanNet++, and ARKitScenes. Source data retains its original copyright and applicable terms. The MindView code license does not relicense these assets.
+
+The publisher has confirmed additional permission for this public redistribution. This hosting permission does not assign a blanket open-data license to the mixed-source package or replace the original source notices.
+
+| Source | Original terms and notices |
+|---|---|
+| ScanNet | [Terms of Use](third_party/ScanNet/TERMS_OF_USE.pdf): noncommercial research and education; the standard terms require recipients to agree before sharing. |
+| ScanNet++ | [Terms of Use](third_party/ScanNetpp/TERMS_OF_USE.pdf): noncommercial research and education; the standard terms require recipients to agree and sign before sharing. |
+| ARKitScenes | [Apple license](third_party/ARKitScenes/LICENSE): retain the applicable notices and comply with its use and redistribution conditions. |
+
+Copies of the original notices accompany this release. Their source URLs and byte fingerprints are recorded in [third_party/SOURCES.json](third_party/SOURCES.json). Additional publisher-held permissions concern this hosted release; downstream uses must respect the terms applicable to those uses.
+
+MindView-authored instructions, sample selection, and experiment mappings are distinct from the source imagery. No blanket license over third-party data is implied. For permissions beyond those explicitly granted by the applicable terms, contact the release authors.
+
+Please cite MindView and the original source datasets when using this resource. Source projects: [ScanNet](https://www.scan-net.org/), [ScanNet++](https://scannetpp.mlsg.cit.tum.de/scannetpp/), and [ARKitScenes](https://github.com/apple/ARKitScenes).
+
+Contact: Yihao Liu (`liuyihao@pjlab.org.cn`) and Jiwen Lu (`lujiwen@tsinghua.edu.cn`).

README.md

@@ -1,46 +1,74 @@
 ---
+license: other
+license_name: mindview-source-dependent-terms
+license_link: https://huggingface.co/datasets/wangyanbo/MindView-Train/blob/main/DATA_LICENSE.md
 language:
 - en
 tags:
 - mindview
-- training-data
 - multi-view
-- spatial-intelligence
-- image-to-image
+- spatial-reasoning
+- image-generation
+- supervised-fine-tuning
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: default
+  default: true
+  data_files:
+  - split: train
+    path: data/train.jsonl
 ---
 
 # MindView-Train
 
-**MindView-Train** is the training-data repository for *MindView: Benchmarking Generative Multi-View Spatial Intelligence*. The training workflow uses language queries and observed views to supervise generation of a target view from the same indoor scene.
+**MindView-Train is the frozen supervised fine-tuning dataset used for MindView's BAGEL adaptation.** It contains **20,000 examples from 3,406 scenes** across ScanNet, ScanNet++, and ARKitScenes. Each example pairs six ordered reference RGB images and a spatial instruction with one held-out target RGB image. Source-aware scene identities are disjoint from [MindView-Bench](https://huggingface.co/datasets/wangyanbo/MindView-Bench).
 
-**Release status:** This repository currently contains documentation only. Training JSONL files and image assets have not been uploaded. Release packaging and access instructions are being prepared.
+[Code and training recipe](https://github.com/Yanbo-23/MindView) · [Benchmark](https://huggingface.co/datasets/wangyanbo/MindView-Bench) · [BAGEL-SFT weights](https://huggingface.co/wangyanbo/MindView-BAGEL-SFT) · [Collection](https://huggingface.co/collections/wangyanbo/mindview-6acf6b7a4a2e25efd51989f5)
 
-## Training-data specification
+## Contents
 
-- **20,000 training examples** in the frozen six-view training specification.
-- **Six ordered context RGB images**, a language query, and **one target RGB image** per example.
-- Coverage of the project's seven target-view generation tasks.
-- Source scene resources from ScanNet, ScanNet++, and ARKitScenes.
+| Path | Purpose |
+|---|---|
+| `data/train.jsonl` | Original 20,000 training records |
+| `data/schema.json` | Required fields and path conventions |
+| `assets/rgb/*.tar` | 104,716 unique RGB images in 58 ordinary TAR archives |
+| `manifests/` | Asset paths, archive membership, sizes, and checksums |
+| `scripts/prepare_data.py` | Download verification and safe extraction |
+| `RELEASE.json`, `SHA256SUMS` | Release inventory and file checksums |
 
-The training JSONL follows `mindview_bagel_single_shot_v1`: `shot` is `6`, `contexts["6"]` contains six relative RGB paths, and `target_image` identifies the separate supervision image. Paths resolve against the configured source-dataset roots.
+Records preserve the `mindview_bagel_single_shot_v1` schema, identifiers, instructions, lineage, and input order. `shot=6`; `contexts["6"]` lists the reference paths, and `target_image` identifies the separate target. Repeated images are stored once. Paths are relative to the corresponding source dataset root.
 
-The training validation protocol requires unique record identities and source lineages, preserves context order, keeps the anchor first when the task requires one, and excludes the target from its context images. It also rejects scene identities shared with the frozen benchmark. This train–evaluation separation must be preserved when preparing downstream splits.
+## Download and prepare
 
-## Related resources
+Read [DATA_LICENSE.md](DATA_LICENSE.md) first. The first complete release is tagged `v1.0`; use this revision for both annotations and assets. Install the Hugging Face CLI if needed:
 
-Collection: [MindView](https://huggingface.co/collections/wangyanbo/mindview-6acf6b7a4a2e25efd51989f5).
+```bash
+pip install -U huggingface_hub
+hf download wangyanbo/MindView-Train \
+  --repo-type dataset --revision "v1.0" \
+  --local-dir ./MindView-Train
+```
 
-| Resource | Purpose |
-|---|---|
-| [MindView-Train](https://huggingface.co/datasets/wangyanbo/MindView-Train) | Six-view supervised training data |
-| [MindView-Bench](https://huggingface.co/datasets/wangyanbo/MindView-Bench) | Frozen evaluation data |
-| [MindView-BAGEL-SFT](https://huggingface.co/wangyanbo/MindView-BAGEL-SFT) | Fine-tuned BAGEL EMA checkpoint |
-| [GitHub project repository](https://github.com/Yanbo-23/MindView) | Project code and usage documentation |
+With Python 3.10 or later, verify the snapshot and extract into a separate directory:
+
+```bash
+python MindView-Train/scripts/prepare_data.py \
+  --repo MindView-Train --verify-only
+python MindView-Train/scripts/prepare_data.py \
+  --repo MindView-Train --output ./mindview-data
+```
+
+Extraction restores `mindview-data/scannet/`, `scannetpp/`, and `arkitscenes/`. Set each runtime dataset root to its corresponding directory. Matching existing files are reused; conflicting files and unsafe archive entries are rejected. Packaging preserves original image bytes without applying training transforms.
+
+For annotations only, use `load_dataset("wangyanbo/MindView-Train", "default", split="train", revision="v1.0")` from the `datasets` package. Records contain paths rather than automatically decoded images; download and extract the TAR assets separately. Pin the same revision for both.
+
+## Training
 
-Code and usage documentation are being prepared for publication at the project repository. Loading instructions, asset manifests, and checksums will be documented with the data release.
+The documented BAGEL recipe uses **10,000 optimizer steps on eight GPUs**. The runtime preserves context order and excludes target images from the inputs; it applies square cropping and BAGEL-specific resizing after decoding. Training additionally requires the matching BAGEL runtime, pretrained weights, model configuration, tokenizer, and VAE. See the project code for the complete recipe. Packaging checks do not constitute a new training run or GPU reproduction.
 
-## Access and terms
+## Terms and contact
 
-Resource access and redistribution terms are being finalized. Source datasets retain their own terms; the code repository's license does not establish a license for the training-data assets. Please refer to the release terms before using or redistributing the eventual files.
+Source copyright and dataset-specific access and usage terms remain applicable. MindView's code license does not relicense source images; see [DATA_LICENSE.md](DATA_LICENSE.md).
 
-For project questions, contact [Yihao Liu](mailto:liuyihao@pjlab.org.cn) or [Jiwen Lu](mailto:lujiwen@tsinghua.edu.cn).
+Contact: Yihao Liu (`liuyihao@pjlab.org.cn`) and Jiwen Lu (`lujiwen@tsinghua.edu.cn`). Include the revision and record ID when reporting issues.

RELEASE.json

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+{
+  "schema_version": "mindview_dataset_release_v1",
+  "prepared_at_utc": "2026-10-15T05:25:52.955617+00:00",
+  "status": "locally_prepared",
+  "destination": {
+    "repo_id": "wangyanbo/MindView-Train",
+    "repo_type": "dataset"
+  },
+  "indices": [
+    {
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+      "sha256": "24d973dc5b2f967d105a6f2a55f46d8f2b0c2d5b262dbd785a60e10ee84627d5"
+    }
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+  },
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+  "archive_inventory": "manifests/asset_archives.json",
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+    "source_stat_version_fields": [
+      "size",
+      "mtime_ns",
+      "ctime_ns"
+    ],
+    "tar_member_path_contract": "dataset-root-relative; extract each dataset's archives into its dataset root",
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+      }
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+      }
+    ]
+  },
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+    "status": "pass",
+    "unique_assets": 104716,
+    "missing_referenced_assets": 0,
+    "unreferenced_assets": 0
+  },
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+    "context_order_nested": true,
+    "targets_excluded_from_inputs": true,
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+    "training_schema_all_rows": true,
+    "training_unique_uids": true,
+    "source_metadata_bytes_preserved": true
+  },
+  "train_test_overlapping_scenes": 0,
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+    "depth_units_converted": false,
+    "source_relative_paths_preserved": true
+  },
+  "scope": "All context and target RGB assets referenced by the frozen 20000-example training snapshot.",
+  "frozen_source": {
+    "name": "mindview_shot6_20000_snapshot.jsonl",
+    "sha256": "24d973dc5b2f967d105a6f2a55f46d8f2b0c2d5b262dbd785a60e10ee84627d5",
+    "record_preservation": "Original annotation bytes, IDs, prompts, targets, and view ordering retained."
+  },
+  "integrity": {
+    "file_checksums": "SHA256SUMS",
+    "member_checksums": "manifests/assets.jsonl",
+    "verification_command": "python scripts/prepare_data.py --repo . --verify-only",
+    "independent_local_verification_report": "Recorded in the release maintainer's local audit, not an assertion of GPU inference or remote-download validation."
+  },
+  "publication_status": "publisher_authorized_public_release",
+  "runtime_status": "schema_and_asset_integrity_verified; training_not_rerun",
+  "publication_authorization": {
+    "status": "additional_public_redistribution_permission_confirmed_by_publisher",
+    "confirmed_on": "2026-10-16"
+  },
+  "release_version": "v1.0",
+  "release_metadata_updated_at_utc": "2026-10-16T06:09:01.698059+00:00",
+  "source_notices": "DATA_LICENSE.md and third_party/SOURCES.json"
+}

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