影像辨識是人工智慧中的熱門領域，可以應用在許多地方，例如手寫數字辨識、車牌辨識、人臉辨識、物體辨識等等。使用深度學習的方法可以有效的提取特徵且降低人力成本，但要創造出一個好的分類模型需要考量很多因素。例如:合適的模型架構，合適的優化方法、合適的參數設定等等。 本實驗的蝴蝶圖像取自ImageNet，且使用卷積神經網路的方法建構蝴蝶辨識模型，並選定幾種可能影響蝴蝶辨識模型的因素作為探討與比較的對象。由實驗結果發現，dropout比例的大小、池化層的大小與擺放位置、相異的優化演算法及相異的卷積層層數皆會影響蝴蝶辨識模型的能力。因此，在建構模型時，這些因素都須慎重選擇，不可忽視它們對模型的影響力。 ;Image recognition is popular in artificial intelligence and can be applied to many fields, such as handwritten digit recognition, license plate recognition, face recognition, object recognition and so on. Using deep learning methods can effectively extract features and reduce costs. But, creating a good classification model requires consideration of many factors. For example: the appropriate model architecture, the appropriate optimization method, the appropriate parameter settings, and so on. The butterfly images of this experiment are taken from ImageNet, and the butterfly identification models are constructed by the convolutional neural network. Several factors that may affect the butterfly identification model are selected as the objects of discussion and comparison. It is observed from the experimental results that the size of the dropout ratio, the size and placement of the pooling layer, the different optimization algorithms and the different layers of convolution layers all affect the ability of the butterfly identification model. Therefore, when constructing the model, these factors must be carefully chosen, and their influence on the model cannot be ignored.
