We follow the code described in the CVPR2023 paper titled "Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval"
Requirements
we use single RTX3090 24G GPU for training and evaluation.
pytorch 1.9.0
torchvision 0.10.0
prettytable
easydict
Organize your data folder as follows:
|-- data/
| |-- train
| |-- train_images/
| |-- train_label.txt
| |-- test
| |-- test_images/
| |-- test_label.txt
| |-- val
| |-- val_images/
| |-- val_label.txt
|-- logs/
| |-- retrival1
| |-- retrival2
The logs can be download from: 链接:https://pan.baidu.com/s/1pcxTXMNkKEaQoXGTPWT7qg 提取码:0wfp
CUDA_VISIBLE_DEVICES=0 python train.py \
--name iira \
--img_aug \
--batch_size 96 \
--MLM \
--loss_names 'sdm+id' \
--dataset_name 'ImageRetri' \
--root_dir './data' \
--num_epoch 30 \
--output_dir logs/retrival1
CUDA_VISIBLE_DEVICES=0 python train.py \
--name iira \
--img_aug \
--batch_size 96 \
--MLM \
--loss_names 'sdm+id+oim' \
--dataset_name 'ImageRetri' \
--root_dir './data' \
--num_epoch 30 \
--output_dir logs/retrieval2
CUDA_VISIBLE_DEVICES=0 python inference_person.py --config_file ./logs/retrival1/configs.yaml
CUDA_VISIBLE_DEVICES=0 python inference_person.py --config_file ./logs/retrival2/configs.yaml
python ensemble.py