wehub-resource-sync
62 lines
2.1 KiB
Markdown
62 lines
2.1 KiB
Markdown
# PSS
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Code for the ECCV '22 submission "PSS: Progressive Sample Selection for Open-World Visual Representation Learning".
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## Dependencies
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We use python 3.7. The CUDA version needs to be 10.2. Besides DGL==0.6.1, we depend on several packages. To install dependencies using conda:
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```commandline
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conda create -n pss python=3.7 # create env
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conda activate pss # activate env
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conda install pytorch==1.7.0 torchvision==0.8.0 cudatoolkit=10.2 -c pytorch # install pytorch 1.7 version
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conda install -y cudatoolkit=10.2 faiss-gpu=1.6.5 -c pytorch # install faiss gpu version matching cuda 10.2
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pip install dgl-cu102 # install dgl for cuda 10.2
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pip install tqdm # install tqdm
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pip install matplotlib # install matplotlib
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pip install pandas # install pandas
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pip install pretrainedmodels # install pretrainedmodels
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pip install tensorboardX # install tensorboardX
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pip install seaborn # install seaborn
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pip install scikit-learn
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cd ..
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git clone https://github.com/yjxiong/clustering-benchmark.git # install clustering-benchmark for evaluation
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cd clustering-benchmark
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python setup.py install
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cd ../PSS
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```
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## Data
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We use the iNaturalist 2018 dataset.
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- download link: https://www.kaggle.com/c/inaturalist-2018/data;
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- annotations are in `Smooth_AP/data/Inaturalist`;
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- annotation txt files for different data splits are in [S3 link]|[[Google Drive](https://drive.google.com/drive/folders/1xrWogJGef4Ex5OGjiImgA06bAnk2MDrK?usp=sharing)]|[[Baidu Netdisk](https://pan.baidu.com/s/14S0Fns29a4o7kFDlNyyPjA?pwd=uwsg)] (password:uwsg).
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Download `train_val2018.tar.gz` and the data split txt files to `data/Inaturalist/` folder. Extract the `tar.gz` files.
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The data folder has the following structure:
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```bash
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PSS
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|- data
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| |- Inaturalist
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| |- train2018.json.tar.gz
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| |- train_val2018.tar.gz
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| |- val2018.json.tar.gz
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| |- train_val2018
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| | |- Actinopterygii
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| | |- ...
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| |- lin_train_set1.txt
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| |- train_set1.txt
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| |- uin_train_set1.txt
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| |- uout_train_set1.txt
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| |- in_train_set1.txt
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| |- Inaturalist_test_set1.txt
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|-...
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```
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## Training
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Run `bash train.sh` to train the model.
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## Test
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Run `bash test.sh` to evaluate on the test set. |