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Add weights for 16 3D cloud instance-segmentation models (8 architectures x 2 conditions)

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README.md ADDED
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+ ---
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+ license: cc-by-nc-4.0
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+ tags:
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+ - segmentation
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+ - instance-segmentation
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+ - 3d
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+ - volumetric
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+ - monai
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+ - atmospheric-science
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+ ---
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+
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+ # 3D Convective-Cloud Instance Segmentation — 8 Architectures
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+
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+ Weights for **16 volumetric segmentation networks** that separate individual
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+ convective clouds in 3D atmospheric-simulation volumes. Eight architectures,
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+ each trained under two data conditions: real volumes only, and real volumes
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+ plus synthetic volumes from the companion
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+ [3D convective-cloud DDPM](https://huggingface.co/HoqueMahmudul/convective-cloud-denoising-diffusion-probabilistic-model-3d).
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+
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+ **Project:** [3D Convective-Cloud Generation collection](https://huggingface.co/collections/HoqueMahmudul/3d-convective-cloud-generation-ddpm-190m-params-6a6ba230c89356ff617d481a)
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+
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+ > **Status:** research artifact from work with an associated manuscript **in
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+ > preparation**. This repository contains **weights only**. Training code,
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+ > methodology and evaluation are not included here and will appear in the
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+ > publication.
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+
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+ ## Contents
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+
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+ ```
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+ <architecture>/<condition>/model.safetensors # weights
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+ <architecture>/<condition>/config.json # constructor args, normalization, inference + post-processing settings
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+ ```
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+
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+ `condition` is `real_only` or `real_plus_synth`. All runs use seed 42; the
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+ saved weights are the checkpoint with the best validation panoptic quality.
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+
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+ | Architecture | Folder | Implementation | Parameters | Loadable from this repo alone |
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+ |---|---|---|---|---|
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+ | 3D U-Net | `unet` | `monai.networks.nets.UNet` | 4.81M | Yes |
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+ | Attention U-Net 3D | `attentionunet` | `monai.networks.nets.AttentionUnet` | 5.91M | Yes |
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+ | SegResNet | `segresnet` | `monai.networks.nets.SegResNet` | 4.70M | Yes |
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+ | SwinUNETR | `swinunetr` | `monai.networks.nets.SwinUNETR` | 15.70M | Yes |
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+ | ResUNet3D | `resunet3d` | custom | 5.91M | No: definition not released |
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+ | Residual Attention U-Net 3D | `resattnunet3d` | custom | 5.93M | No: definition not released |
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+ | SegFormer3D | `segformer3d` | custom (not Perera et al. 2024) | 2.64M | No: definition not released |
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+ | U-Mamba | `umamba` | custom, built on `mamba_ssm` kernels (not Ma et al. 2024) | 6.50M | No: definition not released |
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+
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+ The four MONAI models can be rebuilt from `config.json` and used directly. The
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+ four custom models are published for archival and reproducibility; their
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+ architecture definitions will be released with the publication.
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+
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+ ## Model contract
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+
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+ | | |
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+ |---|---|
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+ | Input | `(B, 1, D, H, W)`, single-channel volume, 0.5 km isotropic voxels |
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+ | Normalization | `x = clip((v - 302) / (1921 - 302), 0, 1)` (background maps to 0) |
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+ | Output | `(B, 3, D, H, W)` **logits**: channel 0 = foreground, 1 = seeds, 2 = boundary |
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+ | Training patch | `(32, 160, 160)` (D, H, W) |
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+ | Full-volume inference | MONAI `sliding_window_inference`, `roi_size=(32,160,160)`, `overlap=0.5`, `mode="gaussian"` blending on logits, then sigmoid |
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+
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+ Instances are obtained by thresholding foreground and seeds, then running a
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+ marker-controlled watershed. `config.json` lists two threshold sets:
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+ `global_operating_point` (shared by all models) and
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+ `calibrated_operating_point` (tuned per model on the validation volume).
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+
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+ ## Usage (MONAI architectures)
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+
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+ ```bash
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+ pip install monai==1.5.2 torch safetensors huggingface_hub
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+ ```
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+
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+ ```python
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+ import json, torch
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+ from huggingface_hub import hf_hub_download
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+ from safetensors.torch import load_file
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+ from monai.networks.nets import SegResNet # or UNet, AttentionUnet, SwinUNETR
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+ from monai.inferers import sliding_window_inference
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+
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+ repo, sub = "HoqueMahmudul/convective-cloud-3d-instance-segmentation", "segresnet/real_only"
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+ cfg = json.load(open(hf_hub_download(repo, f"{sub}/config.json")))
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+ weights = load_file(hf_hub_download(repo, f"{sub}/model.safetensors"))
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+
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+ # Weights were saved from a thin wrapper, so keys carry a "network." prefix.
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+ weights = {k.removeprefix("network."): v for k, v in weights.items()}
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+
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+ model = SegResNet(spatial_dims=3, in_channels=1, out_channels=3, **cfg["model_kwargs"])
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+ model.load_state_dict(weights, strict=True)
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+ model.eval()
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+
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+ lo, hi = cfg["input_normalization"]["lo"], cfg["input_normalization"]["hi"]
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+ volume = torch.from_numpy(raw).float() # raw: (D, H, W) numpy array
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+ x = ((volume - lo) / (hi - lo)).clamp(0, 1)[None, None]
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+
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+ with torch.no_grad():
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+ logits = sliding_window_inference(x, roi_size=(32, 160, 160), sw_batch_size=1,
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+ predictor=model, overlap=0.5, mode="gaussian")
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+ probs = torch.sigmoid(logits)[0] # (3, D, H, W): foreground, seeds, boundary
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+ ```
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+
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+ This loading recipe was verified with MONAI 1.5.2 and PyTorch 2.5.1 for all
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+ four MONAI architectures (`strict=True`).
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+
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+ ## Limitations
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+
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+ - Trained on a small number of real simulation volumes from a single
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+ simulation setup; not validated on other models, resolutions or observational
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+ data.
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+ - One training seed per configuration.
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+ - Not a weather-forecasting or nowcasting tool.
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+
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+ ## License
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+
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+ CC BY-NC 4.0, same as the companion DDPM.
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+ {
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+ "architecture": "attentionunet",
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+ "implementation": "monai.networks.nets.AttentionUnet",
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+ "spatial_dims": 3,
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+ "in_channels": 1,
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+ "out_channels": 3,
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+ "model_kwargs": {
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+ "channels": [
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+ 16,
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+ 256
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+ ],
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+ "strides": [
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+ ]
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+ },
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+ "state_dict_note": "MONAI networks were wrapped, so keys carry a 'network.' prefix; strip it to load into the bare MONAI class.",
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+ "output_channels": [
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+ "foreground",
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+ "seeds",
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+ "boundary"
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+ ],
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+ "output_activation": "logits (apply sigmoid)",
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+ "condition": "real_only",
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+ "condition_label": "Real only volumes",
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+ "seed": 42,
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+ "train_volumes": [
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+ "real_253",
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+ "real_255",
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+ "real_257",
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+ "real_259",
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+ "real_261"
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+ ],
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+ "validation_volume": "real_263",
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+ "selected_step": 8000,
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+ "selection": "best validation panoptic quality on real_263",
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+ "best_val_pq": 0.6819144096495348,
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+ "parameter_count": 5909147,
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+ "input_normalization": {
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+ "method": "x = clip((v - lo) / (hi - lo), 0, 1)",
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+ "lo": 302.0,
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+ "hi": 1921.0
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+ },
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+ "voxel_spacing_km": 0.5,
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+ "patch_size_dhw": [
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+ 32,
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+ "sliding_window": {
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+ "roi_size": [
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+ "overlap": 0.5,
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+ "mode": "gaussian",
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+ "blend": "logits, then sigmoid"
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+ "postprocessing": "foreground/seed thresholding + marker-controlled watershed",
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+ "global_operating_point": {
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+ "source_checkpoint_sha256": "91700d9947d23bc3f89c630f56f5c3886dab9f70e8f836c44c509aa7c233d3df"
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+ }
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+ "boundary"
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+ "hi": 1921.0
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