gr-neutro-cbm-weights

nabimu9/gr-neutro-cbm-weights

gr-neutro-cbm-weights is a image classification model from nabimu9. Use it when you need a label for an image. It is set up for pytorch. The card lists the license as cc-by-4.0.

Weights for neutrophil-morphology classification on GR-Neutro (in-house Gustave Roussy peripheral-blood neutrophil corpus; 4,378 cells, 7 abnormality classes, 10 textbook morphology concepts). Backbone: DinoBloom-B (V…

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Updated Jun 10, 2026

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1.8 GB

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.pt1.7 GB · 98%

At a glance

Task: Image Classification
Library: pytorch
License: cc-by-4.0
Access: Public
Created: Jun 10, 2026
Updated: Jun 10, 2026
SHA: 87fe5f29

Model card

From the Hugging Face model README

GR-Neutro — concept-bottleneck model weights

Code, training/inference/preprocessing instructions: https://github.com/nabilmouadden/biologically-constrained-classification/tree/release/models-code

Download

hf download nabimu9/gr-neutro-cbm-weights --local-dir weights

End-to-end checkpoints

DinoBloom-B last-6 fine-tuned, seed 42, GR-Neutro 7-class test split.

File	Architecture	Test W-F1	Macro-F1	Subset acc.	Multi-seed mean W-F1
`joint_cbm_dinobloomB_ft_s42.pt`	Joint CBM (classifier ∥ concept adapter + constraint)	0.912	0.861	0.865	0.886 ± 0.014
`pure_bottleneck_cbm_dinobloomB_ft_s42.pt`	Pure-bottleneck CBM (class only through concepts, λ=2)	0.909	0.862	0.847	≈ 0.88
`backbone_baseline_dinobloomB_ft_s42.pt`	No-concept backbone baseline	0.907	0.865	0.868	0.890
`cbm_sequential_dinobloomB_ft_s42.pt`	Sequential CBM	0.873	0.804	0.756	—
`cbm_independent_dinobloomB_ft_s42.pt`	Independent CBM	0.772	0.748	0.585	—

Per-abnormality F1 (seed 42)

Class (n)	Joint CBM	Pure-bottleneck	Backbone baseline	Sequential	Independent
Normal (199)	0.987	0.980	0.982	0.962	0.791
Hypogranulation (108)	0.903	0.910	0.897	0.888	0.824
Hyposegmentation (67)	0.855	0.826	0.821	0.846	0.786
Chromatin (35)	0.694	0.750	0.719	0.429	0.437
Hypersegmentation (19)	0.895	0.919	0.919	0.919	0.872
Döhle (19)	0.848	0.757	0.848	0.743	0.684
Hypergranulation (16)	0.842	0.889	0.865	0.842	0.842

Each .pt is a torch.save dict with state_dict, args (incl. mode, unfreeze_last_n, λ's, seed), class_names, concepts, prior_C, normal_idx. infer.py reads args["mode"] and rebuilds the architecture.

Head checkpoints — `heads/`

Concept heads trained on a feature bank, seed 42. Test W-F1:

File (`heads/`)	Method	Frozen bank	Fine-tuned bank
`cem_<bank>_s42_head.pt`	CEM (Concept Embedding Model)	0.844	0.936
`backbone_mlp_<bank>_s42_head.pt`	backbone-MLP reference	0.832	0.935
`pcbmh_<bank>_s42_head.pt`	PCBM-h (residual CBM, r=10)	0.823	0.928
`pure_bottleneck_<bank>_s42_head.pt`	pure-bottleneck head	0.781	0.890

<bank> ∈ {frozen, ft_last4}. Each .pt holds state_dict, constructor_kwargs, class_names, concept_names, train_idx, test_idx, test_wf1. Rebuild with residual_cbm.py's PCBMh / CEM classes.

Feature banks

File	Content	Shape
`dinobloom_b_frozen_features.npz`	Frozen DinoBloom-B CLS features	`features` (4378, 768), `paths`
`dinobloom_b_ft_last4_features.npz`	Fine-tuned (last-4) CLS bank, seed 0	`features` (4378, 768), `paths`

License

CC BY 4.0. Trained weights only; GR-Neutro raw images are not redistributed.

Task: Image Classification
Library: pytorch
License: cc-by-4.0
Created: Jun 10, 2026
Updated: Jun 10, 2026