Gr Neutro Cbm Weights

--- license: cc-by-4.0 tags: - hematology - cell-classification - concept-bottleneck - interpretability - dinobloom library_name: pytorch pipeline_tag: image-classification --- # GR-Neutro — concept-bottleneck model weights 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 (ViT-B/14, 768-d CLS), last-6 blocks fine-tuned for the end-to-end checkpoints. Code, training/inference/preprocessing instructions: https://github.com/nabilmouadden/biologically-constrained-classification/tree/release/models-code ## Download ```bash 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...