An image classification method, comprising: inputting a to-be-classified image into a plurality of different neural network models, to obtain data output by a plurality of non-input layers specified by each neural network model to generate a plurality of corresponding image features; respectively inputting the plurality of image features into a linear classifier, configured to determine a preset class, corresponding to each neural network model, to obtain a corresponding probability that the to-be-classified image comprises an object image of the preset class, the linear classifier being obtained by training according to a feature extracted by a corresponding neural network model from a corresponding training image; and determining, according to each obtained probability, whether the to-be-classified image comprises the object image of the preset class characterized in that the inputting a to-be-classified image into a plurality of different neural network models, to obtain data output by a plurality of non-input layers specified by each neural network model to generate a plurality of corresponding image features comprises: separately inputting images of the plurality of sizes into each neural network model, wherein the images of the plurality of sizes are all obtained by scaling up or down the to-be-classified image at a same aspect ratio; obtaining vectors output by a plurality of layers specified in an intermediate layer and an output layer of each neural network model; and stitching vectors of different layers corresponding to images of a same size of each neural network model and averaging vectors corresponding to images of different sizes, to obtain a plurality of image features separately corresponding to each neural network model. inputting a to-be-classified image into a plurality of different neural network models, to obtain data output by a plurality of non-input layers specified by each neural network model to generate a plurality of corresponding image features; respectively inputting the plurality of image features into a linear classifier, configured to determine a preset class, corresponding to each neural network model, to obtain a corresponding probability that the to-be-classified image comprises an object image of the preset class, the linear classifier being obtained by training according to a feature extracted by a corresponding neural network model from a corresponding training image; and determining, according to each obtained probability, whether the to-be-classified image comprises the object image of the preset class characterized in that the inputting a to-be-classified image into a plurality of different neural network models, to obtain data output by a plurality of non-input layers specified by each neural network model to generate a plurality of corresponding image features comprises: separately inputting images of the plurality of sizes into each neural network model, wherein the images of the plurality of sizes are all obtained by scaling up or down the to-be-classified image at a same aspect ratio; obtaining vectors output by a plurality of layers specified in an intermediate layer and an output layer of each neural network model; and stitching vectors of different layers corresponding to images of a same size of each neural network model and averaging vectors corresponding to images of different sizes, to obtain a plurality of image features separately corresponding to each neural network model. separately inputting images of the plurality of sizes into each neural network model, wherein the images of the plurality of sizes are all obtained by scaling up or down the to-be-classified image at a same aspect ratio; obtaining vectors output by a plurality of layers specified in an intermediate layer and an output layer of each neural network model; and stitching vectors of different layers corresponding to images of a same size of each neural network model and averaging vectors corresponding to images of different sizes, to obtain a plurality of image features separately corresponding to each neural network model.