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Arty CNN-RNN

Multi-head ResNet-50 backbone with column pooling, a bidirectional LSTM over spatial strips, and linear heads for genre, style, and artist on a WikiArt subset (pdjota/artyset).

Evaluation (test split)

Metric	Value
Genre (top-1)	67.80%
Style (top-1)	65.05%
Artist (top-1)	57.71%
Artist (top-5)	88.47%

Checkpoint (local eval): checkpoints/cnnrnn/best.pt — on Hub this repo typically ships as best_model.pt.
Arch: cnnrnn
Epoch (from checkpoint): 0
Test images: 2438

Files on this model repo

Typical layout after upload:

best_model.pt — PyTorch checkpoint (model_state_dict, n_genre / n_style / n_artist, optional arch)
genre_id2label.json, style_id2label.json, artist_id2label.json — class index → label for demos

Limitations

Not for production attribution or forensic ID; academic / demo use.

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Dataset used to train pdjota/arty-cnn-rnn

Spaces using pdjota/arty-cnn-rnn 2

Evaluation results

Genre accuracy (top-1) on pdjota/artyset
Local eval (scripts/eval_cnn.py)

0.678
Style accuracy (top-1) on pdjota/artyset
Local eval (scripts/eval_cnn.py)

0.651
Artist accuracy (top-1) on pdjota/artyset
Local eval (scripts/eval_cnn.py)

0.577
Artist accuracy (top-5) on pdjota/artyset
Local eval (scripts/eval_cnn.py)

0.885