Comments (2)
Thanks for your question. Here are the responses:
- The DISFA model is trained from the best BP4D model. In our experiments, we use the model trained on the combination of BP4D part1 and part3. We do not use the whold BP4D dataset.
- The results of our trained DISFA models (3-fold cross-validation) are:
f1_score accuracy mean_error failure_rate
0.577556 0.929593 0.043427 0.000000
0.656207 0.939148 0.044319 0.000099
0.671716 0.950716 0.042331 0.000162
==============================
0.635159666666667 0.939819000000000 0.0433590000000000 8.70000000000000e-05 - The local f1 score is generally worse than f1 score.
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Thank you for your answer!
It helps me a lot
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Related Issues (20)
- Problem on the evaluation phase of DISFA Dataset HOT 1
- DISFA_combine_1_2_, 1_3, naming convention ? HOT 2
- DISFA action Unit HOT 1
- Train from scratch on DISFA HOT 1
- The performance of BP4D dataset HOT 1
- The performance of BP4D dataset HOT 1
- Could you provide the trained model? HOT 1
- DISFA_combine_1_2_weight.txt not found. HOT 4
- License for using the repository HOT 1
- DISFA AU_corr.txt template HOT 1
- 'all_pred_land' negative value appears HOT 1
- F1_Frame calculation HOT 6
- 无法复现 DISFA 数据集的测试结果 HOT 3
- Unable to configure DISFA as required HOT 2
- preproccess
- path_land.txt是指那个时刻的下的landmark的坐标?
- Use JAANet for inference on my dataset
- Pre-training model error
- How to process the image with empty landmark
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