Lifestyle, Americas, Special Report

[kinoteatrzarya]

The baseline method uses a 3D CNN inspired from (Martin et al., 2019a, 2018). We adjusted the method to answer both proposed subtasks of this year’s edition (Martin et al., 2021): stroke detection and stroke classification from videos of the TTStroke-21 corpus. In order to perform classification and detection, we consider the model architecture presented in Fig. 1. For each subtask, a distinct model has been trained on the train set. We train both using a stochastic gradient approach with a Nesterov momentum of 0.5 (Nesterov, 2004), a weight decay of 0.005 (Hanson and Pratt, 1988) and a constant learning rate of 0.0001. Both models are trained over 500 epochs. The objective function is the cross-entropy loss of the output processed by the softmax function (eq. At each epoch, the model is validated on the validation set. The model performing the best on this set is saved. Then evaluated on the test set. For the detection task, we inferred Non-stroke segments from the annotated Stroke segments.




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[wangiskahn2]

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