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library_name: pytorch
license: mit
pipeline_tag: unconditional-image-generation
tags:
- hdtree
- pytorch
- mnist
- single-cell
- clustering
---
# HDTree ICML Checkpoints
This repository hosts pretrained checkpoints for the model presented in the paper [HDTree: Generative Modeling of Cellular Hierarchies for Robust Lineage Inference](https://huggingface.co/papers/2506.23287).
HDTree is a generative modeling framework designed for robust lineage inference. It captures tree relationships within a hierarchical latent space using a unified hierarchical codebook and employs a quantized diffusion process to model continuous cell state transitions.
- **Code:** [https://github.com/zangzelin/code_HDTree_icml](https://github.com/zangzelin/code_HDTree_icml)
- **Project Page:** [https://zangzelin.github.io/code_HDTree_icml/](https://zangzelin.github.io/code_HDTree_icml/)
## Files
| File | Dataset | Configuration | Notes |
|---|---|---|---|
| `checkpoints/mnist/hdtree_mnist_best_epoch59_acc0.97570.pth` | MNIST | `configs/mnist.yaml` | Best MNIST checkpoint from the full run by checkpoint validation accuracy. |
| `checkpoints/limb/hdtree_limb_i10_epoch199_acc0.53921.pth` | Limb | `configs/limb.yaml` default | Limb sweep i10/default checkpoint. |
## Sample Usage
To validate a trained checkpoint using the official code, you can use the provided validation script:
```bash
# Example for MNIST
bash scripts/validate_checkpoint.sh mnist checkpoints/mnist/hdtree_mnist_best_epoch59_acc0.97570.pth
```
To compute reconstruction and log-likelihood with diffusion sampling, enable generation using the following command:
```bash
python main.py validate \
-c configs/mnist.yaml \
--model.init_args.ckpt_path=checkpoints/mnist/hdtree_mnist_best_epoch59_acc0.97570.pth \
--model.init_args.training_str=step2_r \
--model.init_args.gen_data_bool=True
```
## Reported Metrics
MNIST full run summary:
| ACC | DP | LP | NMI |
|---:|---:|---:|---:|
| 0.97310 | 0.93262 | 0.97310 | 0.92999 |
Limb i10 run summary (`batch_size=1000`, `K=10`, `exaggeration_lat=0.5`, `nu_lat=0.3`):
| ACC | DP | LP | NMI |
|---:|---:|---:|---:|
| 0.52860 | 0.41029 | 0.58370 | 0.49042 |
The included `logs/` files contain the original run outputs used to record these metrics.
## Download
```bash
pip install huggingface_hub
huggingface-cli download zelinzang/HDTree-ICML-checkpoints --local-dir .
```
## Checksums
See `SHA256SUMS`. |