The evaluation metric for this competition is
[Mean F1-Score](https://www.kaggle.com/wiki/MeanFScore)
. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives (tp) to all predicted positives (tp + fp). Recall is the ratio of true positives to all actual positives (tp + fn). The F1 score is given by:
\[ F1 = 2\frac{p \cdot r}{p+r}\ \ \mathrm{where}\ \ p = \frac{tp}{tp+fp},\ \ r = \frac{tp}{tp+fn} \]
The F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.
Formato da submissão
Para cada imagem de card no dataset
, o arquivo de submissão deve conter 2 colunas: image_id e label. Label deve ser uma string com um dos seguintes valores: [JVV,M10,M11,M12,M13,M14]. Cada image_id deve conter um único label.
O arquivo csv deve conter um cabeçalho e seguir o seguinte formato:
image_id,label
14d42990-82ac-11eb-99c2-c57cae5dfade.jpg, M10