Though current CV models have been able to achieve high levels of accuracy on small-scale images classification dataset with hundreds or thousands of categories, many models become infeasible in computational or space consumption when it comes to large-scale dataset with more than 50,000 categories. In this paper, we provide a viable solution for classifying large-scale species datasets using traditional CV techniques such as.features extraction and processing, BOVW(Bag of Visual Words) and some statistical learning technics like Mini-Batch K-Means,SVM which are used in our works. And then mixed with a neural network model. When applying these techniques, we have done some optimization in time and memory consumption, so that it can be feasible for large-scale dataset. And we also use some technics to reduce the impact of mislabeling data. We use a dataset with more than 50, 000 categories, and all operations are done on common computer with l 6GB RAM and a CPU of 3. OGHz. Our contributions are: 1) analysis what problems may meet in the training processes, and presents several feasible ways to solve these problems. 2) Make traditional CV models combined with neural network models provide some feasible scenarios for training large-scale classified datasets within the constraints of time and spatial resources.

翻译：尽管当前计算机视觉模型在数百或数千个类别的小规模图像分类数据集上已能实现较高的准确率，但当面对超过50,000个类别的大规模数据集时，许多模型在计算或空间消耗上变得不可行。本文提出了一种利用传统计算机视觉技术（如特征提取与处理、BOVW（视觉词袋））以及统计学习方法（如我们工作中使用的Mini-Batch K-Means、SVM）对大规模物种数据集进行分类的可行方案，并结合神经网络模型进行混合处理。在应用这些技术时，我们对时间和内存消耗进行了优化，使其能够适用于大规模数据集。同时，我们还采用了一些技术来减少错误标注数据的影响。我们在一个包含超过50,000个类别的数据集上进行了实验，所有操作均在配备16GB内存和3.0GHz CPU的普通计算机上完成。我们的贡献包括：1）分析了训练过程中可能遇到的问题，并提出了几种可行的解决方案；2）将传统计算机视觉模型与神经网络模型相结合，为在时间和空间资源受限的条件下训练大规模分类数据集提供了可行的应用场景。