Update README.md

README.md

@@ -88,35 +88,6 @@ truncated or altered file is detected rather than trusted.
 Commercial use, redistribution and modification are permitted; the only condition is attribution —
 credit the QuantEM authors, link to the licence, and indicate if you made changes.
 
-The `quantem-core` and `napari-quantem` **code** is BSD-3-Clause, which is a separate licence on a
-separate thing.
-
-CC BY rather than a software licence because weights are not source code: the "source and binary
-form" language of BSD does not map onto a state dict. CC BY is also what most of the underlying
-annotation data uses, which keeps the attribution chain coherent.
-
-### Position on training data
-
-Training data for the released heads is CC0 or CC BY 4.0 with the exceptions below. Two sources
-carry copyleft rather than field-of-use terms:
-
-| Source | Licence | Share of that organelle's train+val crops |
-|---|---|--:|
-| DeepContact | GPL-3.0 (applied to data) | mito 5.6 %, ER 11.4 % |
-| ASEM / Incasem | CC BY-SA 4.0 | mito 2.5 %, ER 4.8 % |
-
-**Our position is that trained weights are not a derivative work or adaptation of the training
-images.** They are statistical parameters, not a reproduction or transformation of any particular
-image. We state this explicitly rather than leaving it implied, and we apply it consistently —
-we do not treat the weights as independent of the data here and as derived from it elsewhere.
-Attribution is given in full below regardless, because attribution is owed to every CC-licensed
-source whether or not ShareAlike is triggered.
-
-Two sources with research-use-only terms — **Lucchi++** (EPFL) and **Kasthuri++**
-(Harvard/Lichtman) — were present in earlier mitochondria runs and have been **removed**; the
-released mitochondria head is retrained without them. A field-of-use restriction binds regardless
-of how the derivative-work question is decided, so those could not be reasoned around.
-
 ## Attribution
 
 Full per-source tables, with tile and crop counts and a DOI for every entry, are published as
@@ -127,27 +98,6 @@ datasets alongside these weights:
 * **[`ArrojoeDrigoLab/quantem-base-model-sources`](https://huggingface.co/datasets/ArrojoeDrigoLab/quantem-base-model-sources)**
   — all 655 datasets in the corpus the base encoder was pretrained on.
 
-Those two tables are generated directly from the manuscript's supplementary tables, so they
-cannot drift from what was actually trained on. They are the authoritative list; this card
-deliberately does not keep a second copy.
-
-**Modifications.** All sources were tiled, cropped and in some cases resampled; annotations were
-consolidated onto a common organelle vocabulary. No source is redistributed here in its original
-form.
-
-**Pretraining corpus.** The QuantEM ViT-B encoder was pretrained on the wider EM corpus described
-in the manuscript — public repositories under their own terms, plus data contributed directly by
-27 authors under CC BY 4.0 for this purpose. Contributors are named in the manuscript
-acknowledgements and Supplementary Table 1.
-
-## Upstream components
-
-| Component | Licence | Notes |
-|---|---|---|
-| `timm` (DINOv3 / DINOv2 architecture code) | Apache-2.0 | `timm/models/eva.py` states its DINOv3 code is a modification of the EVA model and is Apache-2.0 like the rest of timm; only *Meta's weights* remain under the DINOv3 licence. **No Meta weights are used or redistributed here.** |
-| QuantEM ViT-B encoder | ours, CC BY 4.0 | Trained **from scratch** on our own EM corpus using the published DINOv3 recipe. No Meta checkpoint was used as initialisation and the released artifact contains no Meta tensors. |
-| OmniEM (EM-DINO) ViT-L encoder | code MIT (`pku-maleilab/omniem-package`) | The four OmniEM heads are built on the encoder published by Lei Ma's group at PKU; please cite OmniEM if you use them. The heads themselves are ours. |
-
 ## Citation
 
 Acree *et al.*, *QuantEM: An optimized platform of vision transformer-based models for