| --- |
| 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`. |
