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AesMamba: Universal Image Aesthetic Assessment with State Space Models (ACM MM 2024)

TODO

  • Add inference code and config files
  • Add checkpoint and script for IAA task

Results

VIAA

FIAA

MIAA

PIAA

Dependencies and Installation

requirements:

  • Linux
  • NVIDIA GPU
  • PyTorch 1.12+
  • CUDA 11.6+

our version(advised)

pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117

pip install --upgrade pip setuptools wheel

install mamba

conda create -n Aesmamba python=3.8 conda activate Aesmamba

git clone https://github.com/state-spaces/mamba.git cd mamba MAMBA_FORCE_BUILD=TRUE pip install .

other requirements

cd ../Aesmamba pip install -r requirements.txt

VIAA task

cd AesMamba_v && python train_viaa.py

MIAA task

cd AesMamba_m && python train_miaa.py

FIAA task

cd AesMamba_f && python train_multi_attr_add_balce.py

PIAA task

cd AesMamba_p && python multi_attr_pred_model_add_human_attr.py.py

Noticing

You can change the config in their corresponding .py file. We will combine the four tasks in our later works.

In our code, we classified the image by its score in each dataset. We uploaded some of their csv files. As for other datasets, we only provide the method of classification because the csv file is large.

Pretrain path

Visual Encoder:vmamba tiny and Text Encoder:bert base We use old version of vmamba, the ckpt is here:

Link: https://pan.baidu.com/s/1REVTVD4w20G7lKnIM-Btjg Passward: c1mk

Vmamba base and it's conda environment please ref https://github.com/MzeroMiko/VMamba