Model Details of qwen35-4b-iconclass-sft-brillfull
qwen35-4b-iconclass-sft-brillfull
Qwen3.5-4B-VL fine-tuned (SFT, 1 epoch) on
davanstrien/iconclass-vlm-brillfull
— the
full-label
iconclass dataset (~4.36 codes/image vs the truncated 3.54).
Why this model exists (research finding)
Built to test whether fixing
truncated training labels
lifts the iconclass classifier
past its ~25% recall ceiling. It does
not
: training converged well (eval_loss 0.47), but
on the clean 788-image full-label test it scores
H-F1 45.3 / hier-recall 46.4 /
code-recall 25.6
— recall unchanged vs models trained on truncated labels.
Conclusion: the 4B is capability-bound
(identifying the right codes), not label-bound —
neither reward tuning nor label completeness moves it.
The approach that
did
improve results is
anchored fusion
: use this model as a precision
anchor
, then a graded VLM-judge gates in extra codes from semantic retrieval. On the same
clean test that lifts results to
H-F1 47.5 / hier-recall 57.6
, with
zero extra
training
.
Base
:
unsloth/Qwen3.5-4B-Base
Recommended use
: as the anchor in the anchored-fusion pipeline (best recall).
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