Fix/vision only forward - #785
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…ed configs Encoder3D used `i > num_single_blocks`, so the default of 2 gave three single-conv blocks. Now `>=`. Default max_channels drops from 256 to 128: the 256-channel blocks sat on 8^3 maps and held most of the parameters without adding anything the earlier stages hadn't already encoded. Both changes alter what a saved config means, so CNN3D configs now carry arch_version (2). CNN3D.upgrade_config maps version-1 configs (no key) onto the current constructor by adding one to num_single_blocks and pinning max_channels=256, so v4a-v4c checkpoints rebuild the exact architecture they trained with. Applied in CNN3D.load and in ArboristVisionMergeDetector.load for the CNN3D backbone. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Vision-only counterpart to ArboristVisionMergeDetector: classifies merge sites from 3D image patches with no graph input, using the CNN3D or ViT3D backbone with its built-in output head. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When modality="image", ThreadedDataLoader yields a plain tensor (not a dict), so x["img"] raised a TypeError. Now accepts both. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
…e cap - _num_neg_candidates: when random_nonmerge_site_prob > 0, return max(n_nonmerge_sites, n_target) so indices above the annotated pool fall through to get_random_nonmerge_site() in get_site() - _list_indices: apply max_brain_examples cap by subsampling both classes proportionally; positives are re-sampled each epoch so all merge sites get coverage over training - create_dataset_collection: accept and forward max_brain_examples; cap is applied to train only (rebalance_classes=True) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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