This is the official PyTorch implementation of the paper:
Gated Transfer Network for Transfer Learning
Yi Zhu and Jia Xue and Shawn Newsam
ACCV 2018
We recommend using a Conda environment. We use PyTorch 1.1, CUDA 9.0 and python 3.7.
conda create -n gtn python=3.7
conda activate gtn
conda install pytorch torchvision cudatoolkit=9.0 -c pytorch
pip install easydictPlease see datasets README for more details.
We take CUB200 as an example in the experiments folder, other experiments are similar except some hyper-parameter changes.
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Set config.py correctly (dataset path, hyper-paramters, etc.)
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python train.py -
Evaluation is done on-the-fly.
Note that, the evaluation performance on UCF101 is not the final results because it is a video dataset. If you need the final clip-level results, you need to perform aggregation (example script can be found here).
If you use this code for your research, please consider citing our paper:
@inproceedings{zhu2018GTN,
author = {Yi Zhu and Jia Xue and Shawn Newsam},
title = {Gated Transfer Network for Transfer Learning},
booktitle = {Asian Conference on Computer Vision (ACCV)},
year = {2018}
}