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PyTorch implementation of One Shot Affordance Detection, completed as a part of the semester long project for the course Advanced Machine Learning (CS5824/ECE5424) at Virginia Tech (Fall-2022).

• One-Shot Affordance Detection (IJCAI2021) (link)

Authors: Hongchen Luo, Wei Zhai, Jing Zhang, Yang Cao, Dacheng Tao

• One-Shot Affordance Detection in the Wild (IJCV) (link)

Authors: Wei Zhai*, Hongchen Luo*, Jing Zhang, Yang Cao, Dacheng Tao

• python 3.7
• pytorch 1.1.0
• opencv
• scipy
• matplotlib

• You can download the PAD from [ Google Drive | Baidu Pan (z40m) ].
• Create a datasets folder, and unzip the PAD dataset there.

You have to download the pretrained resnet50 model from [ Google Drive | Baidu Pan (xjk5) ], then move it to the newly created models folder. Create save_models folder in the OSAD directory before training the model. Remember that in the OSAD directory os_ad_1.py, os_ad_2.py, and os_ad_3.py are three different models trained by us. Whichever model you want to train, just rename that model as os_ad.py. To train the models, execute run_os_ad.py script using the following command:

python run_os_ad.py   

To test these models, execute run_os_ad.py script. Make sure in the save_models folder created before training the models, you are able to see model weights after each epoch:

python run_os_ad.py  --mode test 

In order to evaluate the results, go to the PyMetrics directory and run the requirements.txt file first by using the bash command:

pip install -r requirements.txt

Then go to the code folder and execute the test_metrics_3.py script:

python test_metrics_3.py

• One-Shot Affordance Detection (IJCAI2021) (link)
• One-Shot Affordance Detection Evaluation (link)

• Aruj Nayak [email protected].
• Trisha Bora [email protected].
• Sandeep Chinnareddy [email protected].
• Akhilesh Marathe [email protected].
• Prayati Dutta [email protected].