UPB HES SO @ PlantCLEF 2017: Automatic Plant Image Identification using Transfer Learning via Convolutional Neural Networks
نویسندگان
چکیده
Recent advances in computer vision have made possible the use of neural networks in large scale image retrieval tasks. An example application is the automated plant classification. However, training a network from scratch takes a lot of computational effort and turns out to be very time consuming. In this paper, we investigate a transfer learning approach in the context of the 2017 PlantCLEF task, for automatic plant image classification. The proposed approach is based on the well-known AlexNet Convolutional Neural Network (CNN) model. The network was fine-tuned using the 2017 PlantCLEF Encyclopedia of Life (EOL) training data, which consists of approximately 260,000 plant images belonging to 10,000 species. The learning process was sped up in the upper layers leaving original features almost untouched. Our best proposed official run scored 0,361 in terms of the Mean Reciprocal Rank (MRR) when evaluated on the test dataset.
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