Monday, February 27th, 2023 - Saturday, March 4th, 2023

It could be concluded that I fixed the cropping problem or I added a new cropping feature. The height of the ROI returned by cv2 is slightly longer than the weight. Therefore, I added half of the height to the weight and I could get a square bounding box.

Then, the cropped square image is fed to the pre-processing. Suprisingly, the landmarks could be detected on every image, which means the detection index shift that I spent a lot of time and effort before is useless now. It was a good news or a bad news somehow.

The new cropping function is applied to both CAS(ME)^2 and SAMM Long Videos dataset.

Built the training and test function for CAS_Test and SAMM_Test dataset that are used in the MEGC2022.

Built a function to store the normalize, augment and .pkl files loading function.

Built training and test .ipynb files for the MEGC2022.

Thanks to the new cropping function the extraction and pre-process were running smoothly.

Let hyperparameters set at the beginning of every .ipynb file. Now I could just set the hyperparameters and then click "Run All" and I could get the prediction.

Wrote the part of describing the Locality Self-Attention and Swin Transformer.

Every macro-expression spotting experiments cost more than three days. What else could I do during the training period?

Monday, February 27th, 2023 - Saturday, March 4th, 2023

It could be concluded that I fixed the cropping problem or I added a new cropping feature. The height of the ROI returned by cv2 is slightly longer than the weight. Therefore, I added half of the height to the weight and I could get a square bounding box.

Then, the cropped square image is fed to the pre-processing. Suprisingly, the landmarks could be detected on every image, which means the detection index shift that I spent a lot of time and effort before is useless now. It was a good news or a bad news somehow.

The new cropping function is applied to both CAS(ME)^2 and SAMM Long Videos dataset.

Built the training and test function for CAS_Test and SAMM_Test dataset that are used in the MEGC2022.

Built a function to store the normalize, augment and .pkl files loading function.

Built training and test .ipynb files for the MEGC2022.

Thanks to the new cropping function the extraction and pre-process were running smoothly.

Let hyperparameters set at the beginning of every .ipynb file. Now I could just set the hyperparameters and then click "Run All" and I could get the prediction.

Wrote the part of describing the Locality Self-Attention and Swin Transformer.

Every macro-expression spotting experiments cost more than three days. What else could I do during the training period?