Datasets and source code for our paper DBFC-Net: A Uniform Framework for Fine-grained Cross-media Retrieval.
- pytorch, tested on [v1.0]
- CUDA, tested on v9.0
- Language: Python 3.6
- Data Preparation
Please visit this dataset.
- Demo model
The trained models of our DBFC-Net framework can be downloaded from (Extraction code 1u1f) Baidu Cloud.
The code is currently tested only on GPU.
-
Prepare audio data
- Put the audio dataset to the
audio_dataset
folder.
python audio.py
- Put the audio dataset to the
-
Training
- Download dataset to the
dataset
folder. - In
main.py
.- modify
model_path
to the path where you want to save your parameters of networks. - modify
lr in params
to0.001
,momentum in paramss
to0.9
. - modify
step_siz in StepLR
to5
,gammam in StepLR
to0.8
.
- modify
python main.py
- Download dataset to the
-
Testing
- If you just want to do a quick test on the model and check the final retrieval performance, please follow the subsequent steps.
Download dataset to the
dataset
folder. - Download the trained models of our work and put it to the
models
folder.
python test.py
- If you just want to do a quick test on the model and check the final retrieval performance, please follow the subsequent steps.
Download dataset to the