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Python影像辨識筆記(二十九):Zero-Shot Classification —  Latent Embedding Feedback and Discriminative…

March 6, 2021 by systems

Yanwei Liu

介紹

這篇論文是目前Zero-Shot Classification的state-of-the-art。

可以參考:https://paperswithcode.com/task/zero-shot-learning、https://paperswithcode.com/sota/zero-shot-learning-on-cub-200-2011

在各個不同Dataset上的實驗結果

使用方式

# 需特別注意,這份程式使用PyTorch 0.3.1版本,且repo裡面提供的安裝方式在我的環境當中會有錯誤產生,因此使用以下的方式來進行安裝及訓練conda update conda# 安裝0.3.1的PyTorch
# 參考https://www.ptorch.com/news/145.html
pip install http://download.pytorch.org/whl/cu90/torch-0.3.1-cp36-cp36m-linux_x86_64.whl
pip install h5py sklearn# Clone程式碼
git clone https://github.com/akshitac8/tfvaegan
cd tfvaegan/zero-shot-images/data/
# dataset的檔案來源
wget http://datasets.d2.mpi-inf.mpg.de/xian/cvpr18xian.zip
unzip cvpr18xian.zip && rm cvpr18xian.zip
wget http://datasets.d2.mpi-inf.mpg.de/xian/xlsa17.zip
unzip xlsa17.zip && rm xlsa17.zip
# 上面兩個檔案下載完成解壓縮後,將各別資料夾中data資料夾內的資料夾檔案{cvpr18xian/data/*}和{xlsa17/data/*},移動到tfvaegan/zero-shot-images/data/ 裡面
cd zero-shot-images# 依照不同的dataset,使用不同的訓練指令
# 記得修改run_dataset名稱_tfvaegan.py裡面的CUDA_VISIBLE_DEVICES=6,改成CUDA_VISIBLE_DEVICES=0。因為我們沒有那麼多顆的GPU,使用0代表用第1顆GPU
python image-scripts/run_cub_tfvaegan.py
python image_scripts/run_awa_tfvaegan.py
python image_scripts/run_flo_tfvaegan.py
python image_scripts/run_sun_tfvaegan.py

取得hmdb51和ucf101的dataset

# 來源
The I3D features (concatenated RGB and Flow) for HMDB51 and UCF101 are provided here. Place the unzipped sub-folders under data folder before running the experiments.
cd zero-shot-actions
HMDB51 : python action_scripts/run_hmdb51_tfvaegan.py
UCF101 : python action_scripts/run_ucf101_tfvaegan.py

Filed Under: Machine Learning

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