79 lines
2.5 KiB
Markdown
79 lines
2.5 KiB
Markdown
# Differentiable Cross Modal Hashing via Multimodal Transformers
|
||
|
||
## Framework
|
||
The main architecture of our method.
|
||
|
||
|
||
We propose a selecting mechanism to generate hash code that will transfor the discrete space into a continuous space. Hash code will be encoded as a $2D$ vector.
|
||
|
||
|
||
## Dependencies
|
||
We use python to build our code, you need to install those package to run
|
||
|
||
- pytorch 1.9.1
|
||
- sklearn
|
||
- tqdm
|
||
- pillow
|
||
|
||
## Training
|
||
|
||
### Processing dataset
|
||
Before training, you need to download the oringal data from [coco](https://cocodataset.org/#download)(include 2017 train,val and annotations), [nuswide](https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html)(include all), [mirflickr25k](https://www.kaggle.com/datasets/paulrohan2020/mirflickr25k)(include mirflickr25k and mirflickr25k_annotations_v080),
|
||
then use the "data/make_XXX.py" to generate .mat file
|
||
|
||
For example:
|
||
> cd COCO_DIR # include train val images and annotations files
|
||
>
|
||
> make mat
|
||
>
|
||
> cp DCMHT/data/make_coco.py mat
|
||
>
|
||
> python make_coco.py --coco-dir ../ --save-dir ./
|
||
|
||
After all mat file generated, the dir of `dataset` will like this:
|
||
~~~
|
||
dataset
|
||
├── base.py
|
||
├── __init__.py
|
||
├── dataloader.py
|
||
├── coco
|
||
│ ├── caption.mat
|
||
│ ├── index.mat
|
||
│ └── label.mat
|
||
├── flickr25k
|
||
│ ├── caption.mat
|
||
│ ├── index.mat
|
||
│ └── label.mat
|
||
└── nuswide
|
||
├── caption.txt # Notice! It is a txt file!
|
||
├── index.mat
|
||
└── label.mat
|
||
~~~
|
||
|
||
### Download CLIP pretrained model
|
||
Pretrained model will be found in the 30 lines of [CLIP/clip/clip.py](https://github.com/openai/CLIP/blob/main/clip/clip.py). This code is based on the "ViT-B/32".
|
||
|
||
You should copy ViT-B-32.pt to this dir.
|
||
|
||
### Start
|
||
|
||
After the dataset has been prepared, we could run the follow command to train.
|
||
> python main.py --is-train --hash-layer select --dataset coco --caption-file caption.mat --index-file index.mat --label-file label.mat --similarity-function euclidean --loss-type l2 --vartheta 0.75 --lr 0.0001 --output-dim 64 --save-dir ./result/coco/64 --clip-path ./ViT-B-32.pt --batch-size 256
|
||
|
||
|
||
## Result
|
||
|
||
|
||
## Acknowledegements
|
||
[CLIP](https://github.com/openai/CLIP)
|
||
|
||
[SSAH](https://github.com/lelan-li/SSAH)
|
||
|
||
[GCH](https://github.com/DeXie0808/GCH)
|
||
|
||
[AGAH](https://github.com/WendellGul/AGAH)
|
||
|
||
[DADH](https://github.com/Zjut-MultimediaPlus/DADH)
|
||
|
||
[deep-cross-modal-hashing](https://github.com/WangGodder/deep-cross-modal-hashing)
|