This repository contains the testing code for the paper "TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning" submitted to TPAMI. We will release all codes of this work later.

We provide trained models (Google Drive) on three different datasets: CUB, SUN, AWA2 in the CZSL/GZSL setting. You can download model files as well as corresponding datasets, and organize them as follows:

.
├── saved_model
│   ├── TransZeroPP_CUB_CZSL.pth
│   ├── TransZeroPP_CUB_GZSL.pth
│   ├── TransZeroPP_SUN_CZSL.pth
│   ├── TransZeroPP_SUN_GZSL.pth
│   ├── TransZeroPP_AWA2_CZSL.pth
│   └── TransZeroPP_AWA2_GZSL.pth
├── data
│   ├── CUB/
│   ├── SUN/
│   └── AWA2/
└── ···

The code implementation of TransZero++ mainly based on PyTorch. All of our experiments run and test in Python 3.8.8. To install all required dependencies:

$ pip install -r requirements.txt

Runing following commands and testing TransZero++ on different dataset:

CUB Dataset:

$ python test.py --config config/CUB_CZSL.json      # CZSL Setting
$ python test.py --config config/CUB_GZSL.json      # GZSL Setting

SUN Dataset:

$ python test.py --config config/SUN_CZSL.json      # CZSL Setting
$ python test.py --config config/SUN_GZSL.json      # GZSL Setting

AWA2 Dataset:

$ python test.py --config config/AWA2_CZSL.json     # CZSL Setting
$ python test.py --config config/AWA2_GZSL.json     # GZSL Setting

Results of our released models using various evaluation protocols on three datasets, both in the conventional ZSL (CZSL) and generalized ZSL (GZSL) settings.

Note: All of above results are run on a server with an AMD Ryzen 7 5800X CPU and a NVIDIA RTX A6000 GPU.

Parts of our codes based on:

• hbdat/cvpr20_DAZLE
• zhangxuying1004/RSTNet

If you have any questions about codes, please don't hesitate to contact us by [email protected] or [email protected].