• (Unofficial) PyTorch implementation of Training Vision Transformers for Image Retrieval(El-Nouby, Alaaeldin, et al. 2021).
• I have not yet achieved exactly the same results as reported in the paper(Differential entropy regularization does not have much effect on In-shop and SOP datasets).

# Python 3.7
pip install -r requirements.txt

• See scripts/train.*.sh

# CUB-200-2011
python main.py \
  --model deit_small_distilled_patch16_224 \
  --max-iter 2000 \
  --dataset cub200 \
  --data-path /data/CUB_200_2011 \
  --rank 1 2 4 8 \
  --lambda-reg 0.7

# Stanford Online Products
python main.py \
  --model deit_small_distilled_patch16_224 \
  --max-iter 35000 \
  --dataset sop \
  --m 2 \
  --data-path /data/Stanford_Online_Products \
  --rank 1 10 100 1000 \
  --lambda-reg 0.7

# In-shop
python main.py \
  --model deit_small_distilled_patch16_224 \
  --max-iter 35000 \
  --dataset inshop \
  --data-path /data/In-shop \
  --m 2 \
  --rank 1 10 20 30 \
  --memory-ratio 0.2 \
  --device cuda:2 \
  --encoder-momentum 0.999 \
  --lambda-reg 0.7

• IRT_O – off-the-shelf extraction of features from a ViT backbone, pre-trained on ImageNet;
• IRT_L – fine-tuning a transformer with metric learning, in particular with a contrastive loss;
• IRT_R – additionally regularizing the output feature space to encourage uniformity.
• †: Models pre-trained with distillation with a convnet trained on ImageNet1k

• El-Nouby, Alaaeldin, et al. "Training vision transformers for image retrieval." arXiv preprint arXiv:2102.05644 (2021).