Implementation of paper - Outfit Transformer: Outfit Representations for Fashion Recommendation
Overall code structure is based on DeepFashion repository.
The figures below are derived using the Polyvore-D (disjoint) test dataset.
| Model | CP(AUC) | FITB(Accuracy) | CIR(Recall@10) |
|---|---|---|---|
| Type-Aware | 0.86 | 57.83 | 3.50 |
| SCE-Net | 0.91 | 59.07 | 5.10 |
| CSA-Net | 0.91 | 63.73 | 8.27 |
| OutfitTransformer(Paper) | 0.93 | 67.10 | 9.58 |
| OutfitTransformer(Implemented) | Testing... | Testing... | Not Trained |
This code is tested with python 3.9.16, torch 1.12.1
python -m pip install -r requirements.txt
Download the polyvore dataset from here
python train.py --task cp --train_batch 64 --valid_batch 96 --n_epochs 5 --learning_rate 1e-5 --scheduler_step_size 1000 --work_dir $WORK_DIR --data_dir $DATA_DIR --wandb_api_key $WANDB_API_KEY
python train.py --task cir --train_batch 64 --valid_batch 96 --n_epochs 5 --learning_rate 1e-5 --scheduler_step_size 1000 --work_dir $WORK_DIR --data_dir $DATA_DIR --wandb_api_key $WANDB_API_KEY --checkpoint $CHECKPOINT
python test.py --task $TASK --polyvore_split nondisjoint --test_batch 96 --data_dir $DATA_DIR --checkpoint $CHECKPOINT
Checkpoints are organized by tasks and timestamps as shown in the following file structure.
You can resume and load from checkpoints.
checkpoints
+-- $TASK
| +-- $YYYY_$mm_$dd
| | +-- $EPOCH_$SCORE.pth
- A paper review of implementation can be found at here. (Only Available in Korean)
- This is NON-OFFICIAL implementation. (The official repo has not been released.)
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