SiDIT base

This repository contains the tensor-only parameters of the historical SiDIT model for joint reflection-family and crystal-property prediction from single-crystal diffraction point clouds.

The inference implementation and numerical preprocessing are available at max6616/SiDIT on GitHub. Recovered test inputs are distributed in max6616/SiD3M-test; consult the dataset card for their exact scope.

Parameters and configuration

sidit-base-state-dict.pt contains 523 state tensors, 194,915,607 bytes in total. It preserves the model tensors associated with the retained cached benchmark and excludes optimizer state and Python argument objects. Loading uses torch.load(path, map_location='cpu', weights_only=True); construct the model with the accompanying config.json and the GitHub implementation.

• Backbone: Point Transformer V3 encoder–decoder, base configuration.
• Per-point classes: 21 classes for each of h, k, and l, corresponding to [-10, 10].
• Global outputs: crystal system, Laue class, point group, space group, and the nine-component cell head.
• Cell head: normalized lengths plus cosine/sine angles; decode in float32 as documented in the implementation.
• The retained checkpoint was saved at global step 80,000. Its tensors are unchanged by this release.

Model-file SHA-256: 22fceed0e9e303e97ce668cf71b092f39ab9446fe38ec350a4189bc97e389ad7.

Scope

The parameters support inference on the supplied simulated point-cloud format. Predicted HKL values are canonical reflection-family labels in that format. The model does not supply a refined orientation matrix or establish an end-to-end experimental indexing result. Public test recovery and cached statistical reconstruction are identified separately in the dataset card.

Authors: Zhao Zhang, Zheng Dong, Zhi Geng, Xinlong Dong, Yi Zhang, and Gaoqi He. Original implementation copyright 2025 Zhang Zhao; MIT license. Pointcept-derived code retains its upstream MIT notice in the software repository.