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Datasets:
synzif8
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SynZIF-8

Tasks:
Object Detection
Keypoint Detection
Image Segmentation
Modalities:
Image
Text
Formats:
webdataset
Size:
1M - 10M
Tags:
Synthetic
SEM
scanning-electron-microscopy
materials-science
ZIF-8
metal-organic-framework
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WebDataset
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metadata 
license: cc-by-4.0
task_categories:
  - object-detection
  - keypoint-detection
  - image-segmentation
tags:
  - synthetic
  - SEM
  - scanning-electron-microscopy
  - materials-science
  - ZIF-8
  - metal-organic-framework
  - 3d-keypoint
  - instance-segmentation
size_categories:
  - 10K<n<100K
pretty_name: SynZIF-8

SynZIF-8: A Synthetic SEM Dataset and Benchmark for 3D Sub-Micron Crystal Perception

Dataset Summary

SynZIF-8 is a synthetic Scanning Electron Microscope (SEM) dataset of rhombic dodecahedron crystals modeled after ZIF-8 metal-organic frameworks. The dataset contains 20,000 images with 455K+ instance annotations and zero human annotation cost, generated by chaining ControlNet and LoRA-stylized diffusion models with deterministic 3D rendering.

Code Repository

The evaluation code, training scripts, and baseline implementations referenced in the SynZIF-8 paper (NeurIPS 2026) are released at:

🔗 https://github.com/synzif8/synzif8

This includes:

  • 3D rendering pipeline (rhombic dodecahedron geometry, Gaussian clustering, orthographic projection, per-instance annotation extraction)
  • SEM stylization (Stable Diffusion 1.5 + LoRA + ControlNet + mask-guided contrast calibration)
  • Mask R-CNN front-end detector (seg_exp07)
  • Nine baseline implementations across three families:
    • 3D keypoint regression: FFB6D, REDE, Uni6D
    • Geometry-aware 6D pose: SC6D, GDR-Net, HccePose
    • Foundation-style 6D pose: MegaPose, GigaPose, FoundationPose
  • Evaluation suite: edge-length RRMSE, MPJPE with per-instance z-offset correction
  • FastAPI annotation tool + chemistry expert annotation notebooks

Trained Baseline Checkpoints

Pre-trained weights for all nine baseline models reported in the paper (Sec. 4) are provided under checkpoints/:

File Model Category Size
checkpoints/ffb6d.pt FFB6D (B) 3D Keypoint Regression 141 MB
checkpoints/rede.pt REDE (B) 3D Keypoint Regression 146 MB
checkpoints/uni6d.pt Uni6D (B) 3D Keypoint Regression 606 MB
checkpoints/sc6d.pt SC6D (A) Geometry-Aware 6D Pose 146 MB
checkpoints/gdrnet.pt GDR-Net (A) Geometry-Aware 6D Pose 155 MB
checkpoints/hccepose.pt HccePose (A) Geometry-Aware 6D Pose 112 MB
checkpoints/megapose.pt MegaPose (C) Foundation-Style 6D Pose 120 MB
checkpoints/gigapose.pt GigaPose (C) Foundation-Style 6D Pose 583 MB
checkpoints/foundationpose.pt FoundationPose (C) Foundation-Style 6D Pose 17 MB

Each file is a PyTorch state_dict (pure model weights, no metadata). Load with torch.load("checkpoints/<model>.pt", map_location="cpu").

Dataset Structure

The dataset is provided as a single tar archive (synzif8_full.tar) containing the dataset_v6/ directory.

Per-image files

For each rendered scene render_{ID}, the following files are provided:

File Description
render_{ID}.png Original rendered image (rhombic dodecahedron crystals)
render_{ID}_styled.png SEM-stylized image (via ControlNet + LoRA)
render_{ID}_edge.png Visible edge map
render_{ID}_labeled.png Labeled visualization with instance IDs
render_{ID}_mask.png Combined instance mask
render_{ID}_masks/ Per-instance amodal and visible masks
render_{ID}_metadata.json 14 vertex coordinates, edge lengths, visibility, per-instance annotations

Splits

Split # Images
Train 16,000
Validation 2,000
Test 2,000

Split is fixed (seed=42) and shared across all benchmark experiments.

Tasks Supported

  • Instance Segmentation (amodal + visible masks)
  • 3D Keypoint Detection (14 vertices per crystal)
  • 6D Pose Estimation
  • Edge Length Measurement (for downstream CO₂ diffusivity computation)

Intended Use

SynZIF-8 is designed as both training data and an evaluation testbed for:

  1. Models tackling SEM image analysis of crystalline particles
  2. Benchmarking automated morphological measurement (edge length) against human expert annotation
  3. Evaluating 3D keypoint detection and 6D pose estimation in sub-micron-scale microscopy domains

License

CC-BY-4.0

Limitations

  • Synthetic-to-real domain gap may persist despite SEM stylization
  • Models exclusively cover ZIF-8-like rhombic dodecahedron geometry
  • A 2-pixel resolution discrepancy exists between rendered (686px) and SEM-styled (688px) images

Citation

To be added upon publication.