Dev

.gitignore

@@ -188,4 +188,13 @@ wandb/
 *.pth
 
 # VS Code
-.vscode/
+.vscode/
+
+# Huggingface
+hf_export/spectre/
+hf_export/imgs/
+hf_export/model.safetensors
+hf_export/config.json
+hf_export/README.md
+hf_export/LICENSE
+hf_export/LICENSE_MODELS

README.md

@@ -1,3 +1,5 @@
+📢 [2026-05-20] The pretrained SPECTRE model can now be loaded directly through the `transformers` library, no separate SPECTRE package installation required. Check below for details and usage examples.
+
 📢 [2026-04-10] SPECTRE is now an official baseline for the [**CVPR 2026 Workshop Competition: Foundation Models for General CT Image Diagnosis**](https://www.codabench.org/competitions/12650/)! See `experiments/cvpr26_fm_for_ct_diag_task_1` for scripts and additional details.  
 
 📢 [2026-02-21] SPECTRE has been accepted for presentation at **CVPR 2026** (Denver, Colorado, USA)!  
@@ -12,8 +14,8 @@
   <a href="https://pypi.org/project/spectre-fm/"><img alt="Python Versions" src="https://img.shields.io/pypi/pyversions/spectre-fm?style=flat-square&cacheSeconds=0" /></a>
   <a href="https://pypi.org/project/spectre-fm/"><img alt="Downloads per Month" src="https://img.shields.io/pypi/dm/spectre-fm?style=flat-square&label=downloads&cacheSeconds=0" /></a>
   <a href="https://github.com/cclaess/SPECTRE/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/github/license/cclaess/SPECTRE?style=flat-square&cacheSeconds=0" /></a>
-  <a href="https://huggingface.co/cclaess/SPECTRE"><img alt="Model weights" src="https://img.shields.io/badge/models-Hugging%20Face-yellow?style=flat-square&cacheSeconds=0" /></a>
-  <a href="https://arxiv.org/abs/2511.17209"><img alt="Paper" src="https://img.shields.io/badge/paper-arXiv-b31b1b?style=flat-square&cacheSeconds=0" /></a>
+  <a href="https://huggingface.co/cclaess/SPECTRE-Large"><img alt="Model weights" src="https://img.shields.io/badge/model-Hugging%20Face-yellow?style=flat-square&cacheSeconds=0" /></a>
+  <a href="https://arxiv.org/abs/2511.17209"><img alt="Preprint" src="https://img.shields.io/badge/preprint-arXiv-b31b1b?style=flat-square&cacheSeconds=0" /></a>
 </p>
 
 <p align="center">
@@ -27,14 +29,25 @@ SPECTRE has been trained on a large cohort of **open-source CT scans** of the **
 This repository provides pretrained SPECTRE models together with tools for fine-tuning and evaluation.
 
 ## 🧠 Pretrained Models
-The pretrained SPECTRE model can easily be imported as follows:
+The pretrained SPECTRE model can easily be imported using the `transformers` library
 
 ```python
-from spectre import SpectreImageFeatureExtractor, MODEL_CONFIGS
-import torch
+from transformers import AutoModel
+model = AutoModel.from_pretrained('cclaess/SPECTRE-Large', trust_remote_code=True)
+```
 
+or by using the `spectre-fm` package as follows:
+
+```python
+from spectre import SpectreImageFeatureExtractor, MODEL_CONFIGS
 config = MODEL_CONFIGS['spectre-large-pretrained']
 model = SpectreImageFeatureExtractor.from_config(config)
+```
+
+A simple forward pass would look like:
+```python
+import torch
+
 model.eval()
 
 # Dummy input: (batch, crops, channels, height, width, depth)
@@ -94,12 +107,40 @@ This repository is organized as follows:
 
 ## ⚙️ Setting Up the Environment
 
-To get up and running with SPECTRE, simply install our package using pip:
+To get up and running with SPECTRE, install the base package with pip:
 
 ```bash
 pip install spectre-fm
 ```
 
+This installs only the runtime dependencies needed to load and run the pretrained models.
+
+If you want to fine-tune or pretrain SPECTRE, install the matching extra:
+
+```bash
+pip install "spectre-fm[training]"
+```
+
+If you only need the evaluation stack, install:
+
+```bash
+pip install "spectre-fm[eval]"
+```
+
+If training on GDS-enabled systems is required, install the CUDA 12 specific extra:
+
+```bash
+pip install "spectre-fm[gds-cuda12]"  # with training stack: "spectre-fm[training,gds-cuda12]"
+```
+
+**Note that** `gds-cuda12` is only compatible with CUDA 12.x environments.
+
+To install everything at once, use:
+
+```bash
+pip install "spectre-fm[all]"
+```
+
 or install the latest updates directly from GitHub:
 
 ```bash

hf_export/configuration_spectre.py

@@ -0,0 +1,32 @@
+from transformers import PretrainedConfig
+
+
+class SpectreConfig(PretrainedConfig):
+    model_type = "spectre"
+
+    def __init__(
+        self,
+        backbone_name="vit_large_patch16_128",
+        backbone_kwargs={
+            "num_classes": 0,
+            "global_pool": '',
+            "pos_embed": "rope",
+            "rope_kwargs": {"base": 1000.0},
+            "init_values": 1.0,
+        },
+        feature_combiner_name="feat_vit_large",
+        feature_combiner_kwargs={
+            "num_classes": 0,
+            "global_pool": "",
+            "pos_embed": "rope",
+            "rope_kwargs": {"base": 100.0},
+            "init_values": 1.0,
+        },
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.backbone_name = backbone_name
+        self.backbone_kwargs = backbone_kwargs or {}
+        self.feature_combiner_name = feature_combiner_name
+        self.feature_combiner_kwargs = feature_combiner_kwargs or {}

hf_export/metadata.yaml

@@ -0,0 +1,13 @@
+license: cc-by-nc-sa-4.0
+language:
+- en
+tags:
+- medical-imaging
+- ct-scan
+- 3d
+- vision-transformer
+- self-supervised-learning
+- foundation-model
+- radiology
+library_name: transformers
+pipeline_tag: feature-extraction

hf_export/modeling_spectre.py

@@ -0,0 +1,41 @@
+import torch
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import BaseModelOutput
+
+from spectre.model import SpectreImageFeatureExtractor
+try:
+    from .configuration_spectre import SpectreConfig
+except ImportError:
+    from configuration_spectre import SpectreConfig
+
+
+class SpectreModel(PreTrainedModel):
+    config_class = SpectreConfig
+    base_model_prefix = "spectre"
+    main_input_name = "pixel_values"
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.model = SpectreImageFeatureExtractor(
+            backbone_name=config.backbone_name,
+            backbone_kwargs=config.backbone_kwargs,
+            feature_combiner_name=config.feature_combiner_name,
+            feature_combiner_kwargs=config.feature_combiner_kwargs,
+        )
+
+        self.post_init()
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        grid_size=None,
+        return_dict=False,
+        **kwargs,
+    ):
+        outputs = self.model(pixel_values, grid_size=grid_size)
+
+        if not return_dict:
+            return outputs
+
+        return BaseModelOutput(last_hidden_state=outputs)

pyproject.toml

@@ -28,27 +28,49 @@ keywords = ["medical", "ct", "self-supervised", "vision", "deep-learning"]
 
 dependencies = [
 	"torch",
-	"wandb",
 	"numpy",
-	"monai[all]",
+	"timm",
+	"transformers",
+	"huggingface_hub",
+	"loralib",
+]
+
+[project.optional-dependencies]
+training = [
+	"wandb",
+	"monai",
 	"nibabel",
 	"SimpleITK",
-	"timm",
 	"scipy",
 	"pandas",
 	"openpyxl",
+	"tqdm",
 	"accelerate",
 	"omegaconf",
-	"transformers",
-	"tokenizers",
-	"peft",
-	"loralib",
+]
+
+eval = [
+	"monai",
+	"nibabel",
+	"SimpleITK",
+	"pillow",
+	"scipy",
+	"scikit-learn",
+	"pandas",
+	"openpyxl",
+	"tqdm",
 	"matplotlib",
 	"seaborn",
-	"scikit-learn",
 	"umap-learn",
 ]
 
+gds-cuda12 = [
+	"cupy-cuda12x",
+	"kvikio",
+]
+
+all = ["spectre-fm[training,eval,gds-cuda12]"]
+
 [project.urls]
 Homepage = "https://github.com/cclaess/SPECTRE"
 Source = "https://github.com/cclaess/SPECTRE"