[ Arxiv ]
- QnA was accepted for Oral Presentation at CVPR 2022
- Implementation of QnA layer and other components are available
- QnA-ViT and training code will be released later this month
- Code went refactoring - under testing and reproducing results
Pretrained models can be downloaded from this link.
| Model | Params | GFLOPs | Top-1 | Warmup |
|---|---|---|---|---|
| QnA_ViT_tiny | 16M | 2.5 | 81.7 | 5 |
| QnA_ViT_tiny_7x7 | 16M | 2.6 | 82.0 | 5 |
| QnA_ViT_small | 25M | 4.4 | 83.2 | 5 |
| QnA_ViT_base | 56M | 9.7 | 83.9 | 20 |
Download the model parameters and copy
CUDA_VISIBLE_DEVICES=0 python3 main.py --eval_only \
--workdir <MODEL_DIR> \
--config configs/imagenet_qna.py \
--config.model_name <MODEL_DIR> \
--config.dataset_version 5.1.0 \
--config.data_dir <DATA_DIR> \
--config.batch_size <BATCH_SIZE> \
--config.half_precision=False
Flags:
- workdir : location to the checkpoints directory
- model_name : the model name, e.g., qna_vit_tiny (see table above for model names - use lowercase names only).
- dataset_version : Tensorflow datasets ImageNet dataset version. Mine was (5.1.0),
you can change according to your installed version.
- data_dir : the location of the ImageNet directory (need to have the validation set)
- batch_size : the evaluation batch size
Please cite our paper if you find this repo helpful:
@InProceedings{Arar_2022_CVPR,
author = {Arar, Moab and Shamir, Ariel and Bermano, Amit H.},
title = {Learned Queries for Efficient Local Attention},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022}
}
