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cnn_v3
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cnn_v3_utils.py
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15 hours
fix(cnn_v3): L1 loss + depth-grad tanh normalization to reduce flat convergence
HEAD
main
skal
3 days
feat(cnn_v3/training): load prev.png when available; document web tool prev gap
skal
3 days
feat(cnn_v3/training): add --single-sample option + doc fixes
skal
5 days
feat(cnn_v3): shadow→dif migration complete (ch18)
skal
5 days
feat(cnn_v3): patch alignment search, resume, Ctrl-C save
skal
6 days
perf(cnn_v3): cache dataset images at init to avoid per-patch disk I/O
skal
6 days
fix(cnn_v3): resize target to albedo dims when sizes differ
skal
6 days
refactor(cnn_v3): code review — comments, simplifications, test fix
skal
7 days
feat(cnn_v3): Phase 6 — training script (train_cnn_v3.py + cnn_v3_utils.py)
skal
