Datasets:

wangyanbo
/

MindView-Train

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
  - multi-view
  - 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 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.

Code and training recipe · Benchmark · BAGEL-SFT weights · Collection

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

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.

Download and prepare

Read 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:

pip install -U huggingface_hub
hf download wangyanbo/MindView-Train \
  --repo-type dataset --revision "v1.0" \
  --local-dir ./MindView-Train

With Python 3.10 or later, verify the snapshot and extract into a separate directory:

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

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.

Terms and contact

Source copyright and dataset-specific access and usage terms remain applicable. MindView's code license does not relicense source images; see DATA_LICENSE.md.

Contact: Yihao Liu (liuyihao@pjlab.org.cn) and Jiwen Lu (lujiwen@tsinghua.edu.cn). Include the revision and record ID when reporting issues.

MindView-Train

Contents

Download and prepare

Training

Terms and contact