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Though current CV models have been able to achieve high levels of accuracy onsmall-scale images classification dataset with hundreds or thousands ofcategories, many models become infeasible in computational or space consumptionwhen it comes to large-scale dataset with more than 50,000 categories. In thispaper, we provide a viable solution for classifying large-scale speciesdatasets using traditional CV techniques such as.features extraction andprocessing, BOVW(Bag of Visual Words) and some statistical learning technicslike Mini-Batch K-Means,SVM which are used in our works. And then mixed with aneural network model. When applying these techniques, we have done someoptimization in time and memory consumption, so that it can be feasible forlarge-scale dataset. And we also use some technics to reduce the impact ofmislabeling data. We use a dataset with more than 50, 000 categories, and alloperations are done on common computer with l 6GB RAM and a CPU of 3. OGHz. Ourcontributions are: 1) analysis what problems may meet in the trainingprocesses, and presents several feasible ways to solve these problems. 2) Maketraditional CV models combined with neural network models provide some feasiblescenarios for training large-scale classified datasets within the constraintsof time and spatial resources.

Approaches of large-scale images recognition with more than 50,000 categoris