Pytorch implementation of "DLI-Net: Dual Local Interaction Network for Fine-Grained Sketch-Based Image Retrieval". DLI-Net is the improved version of "DLA-Net for FG-SBIR: Dynamic Local Aligned Network for Fine-Grained Sketch-Based Image Retrieval". So you can also refer this code to implement DLA-Net.
- python 3.8
- pytorch 1.9
There are six datasets used in our paper. To train our DLI-Net, you need change the parameters of *.sh (e.g. ph_train_root and ph_train_txt). It is important to note that the folder structures of datasets need to be consistent with ours, otherwise you need to modify the code accordingly. Our folder structures are shown below.
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QMUL-Shoe-v1/QMUL-Chair-v1/QMUL-Handbag
dataset_name ├── photo │ ├── 1.jpg │ └── ... ├── sketch │ ├── 1.png │ └── ... ├── photo_test_name.txt ├── photo_train_name.txt ├── sketch_test_name.txt └── sketch_train_name.txt -
QMUL-Shoe-v2/QMUL-Chair-v2
dataset_name ├── photo_test.txt ├── photo_train.txt ├── sketch_test.txt ├── sketch_train.txt ├── testA │ ├── 2429245009_1.png │ └── ... ├── testB │ ├── 2429245009.png │ └── ... ├── test_noise │ ├── 1445100078_2_mask1.png │ └── ... ├── trainA │ ├── 1031000079_1.png │ └── ... ├── trainB │ ├── 1031000079.png │ └── ... └── train_noise ├── 1445100078_2_mask1.png └── ... -
Sketchy
For this dataset, we first remove the invalid images based on the files in the folder of
info.Sketchy ├── info │ ├── invalid-ambiguous.txt │ ├── invalid-context.txt │ ├── invalid-error.txt │ ├── invalid-pose.txt │ ├── README.txt │ ├── stats.csv │ ├── strokes.csv │ ├── testset.txt │ └── urls.txt ├── photo │ └── tx_000100000000 │ ├── split_test_train.py │ ├── test │ │ ├── airplane │ │ │ ├── n02691156_1512.jpg │ │ │ └── ... │ │ └── ... │ └── train │ ├── airplane │ │ ├── n02691156_58.jpg │ │ └── ... │ └── ... ├── sketch │ └── tx_000100000000 │ ├── test │ │ ├── airplane │ │ │ ├── n02691156_1512-2.png │ │ │ └── ... │ │ └── ... │ └── train │ ├── airplane │ │ ├── n02691156_58-1.png │ │ └── ... │ └── ... ├── label_dict.npy ├── photo_seen_test.txt ├── photo_seen_train.txt ├── photo_test_relative_path.txt ├── photo_train_relative_path.txt ├── photo_unseen.txt ├── sketch_seen_test.txt ├── sketch_test_relative_path.txt ├── sketch_unseen.txt └── CC_label_dict.npy
First modify the parameters in *.sh files, then use bash *.sh to train model.