/ZSL_Generative

Generative paper implementations for Zero Shot Learning

Primary LanguagePython

ZSL_Generative

<<<<<<< HEAD An open source Zero Shot Classification toolbox based on PyTorch.

Installation

The model is built in PyTorch 1.6.0 and tested on Ubuntu 16.04 environment (Python3.7, CUDA11.0, cuDNN7.5).

For installing, follow these intructions

conda create -n pytorch160 python=3.7
conda activate pytorch160
conda install pytorch=1.6 torchvision cudatoolkit=11.0 -c pytorch
pip install -r requirements.txt

Data preparation

For downloading v1 version of CUB, AWA2, FLO and SUN features use download.sh inside datasets/v1 folder.

cd datasets/v1; sh download.sh; cd ../../

For downloading v2 version of CUB, AWA2, and SUN features use download.sh inside datasets/v2 folder.

cd datasets/v2; sh download.sh; cd ../../

Training and Evaluation

Zero-Shot Image Classification

To train and evaluate ZSL and GZSL models on v1 version of CUB, AWA2, FLO and SUN, please run:

CUB: python train_images.py -opt options/Tfvaegan/CUB.yml
AWA2: python train_images.py -opt options/Tfvaegan/AWA2.yml
FLO: python train_images.py -opt options/Tfvaegan/FLO.yml
SUN: python train_images.py -opt options/Tfvaegan/SUN.yml

To train and evaluate ZSL and GZSL models on v2 version of CUB, AWA2, and SUN, edit configuration file.

version: v2

Wandb Logger

wandb can be viewed as a cloud version of tensorboard. One can easily view training processes and curves in wandb.
To enable wandb logging edit the configuration file.

wandb: True

||||||| merged common ancestors Generative paper implementations for Zero Shot Learning

An open source Zero Shot Classification toolbox based on PyTorch.

Installation

The model is built in PyTorch 1.6.0 and tested on Ubuntu 16.04 environment (Python3.7, CUDA11.0, cuDNN7.5).

For installing, follow these intructions

conda create -n pytorch160 python=3.7
conda activate pytorch160
conda install pytorch=1.6 torchvision cudatoolkit=11.0 -c pytorch
pip install -r requirements.txt

Data preparation

Download CUB, AWA, FLO and SUN features using downlaod.sh inside datasets folder.

sh download.sh

Training and Evaluation

Zero-Shot Image Classification

To train and evaluate ZSL and GZSL models on CUB, AWA, FLO and SUN, please run:

CUB: python train_images.py -opt options/Tfvaegan/CUB.yml
AWA: python train_images.py -opt options/Tfvaegan/AWA.yml
FLO: python train_images.py -opt options/Tfvaegan/FLO.yml
SUN: python train_images.py -opt options/Tfvaegan/SUN.yml

Wandb Logger

wandb can be viewed as a cloud version of tensorboard. One can easily view training processes and curves in wandb.
To enable wandb logging edit the configuration file.

wandb: True

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