import * as tf from '@tensorflow/tfjs';
import { ImageModel } from './model';
import { ClassificationResult } from '../classifier-utils';
export type ClassifierModel = Awaited<ReturnType<typeof loadImageClassifierModel>>;
export declare function loadImageClassifierModel(options: {
    baseModel: ImageModel;
    hiddenLayers?: number[];
    modelUrl?: string;
    cacheUrl?: string;
    checkForUpdates?: boolean;
    /** @description if not provided, will be auto scanned from datasetDir or load from the model.json */
    classNames?: string[];
}): Promise<{
    baseModel: {
        spec: import("..").ImageModelSpec;
        model: tf.GraphModel<string | tf.io.IOHandler>;
        fileEmbeddingCache: Map<string, tf.Tensor<tf.Rank>> | null;
        checkCache: (url: string) => tf.Tensor | void;
        loadImageCropped: (url: string) => Promise<tf.Tensor4D & tf.Tensor<tf.Rank>>;
        imageUrlToEmbedding: (url: string) => Promise<tf.Tensor>;
        imageFileToEmbedding: (file: File) => Promise<tf.Tensor>;
        imageTensorToEmbedding: (imageTensor: import("..").ImageTensor) => tf.Tensor;
    };
    classifierModel: (tf.Sequential & {
        classNames?: string[];
    }) | (tf.LayersModel & {
        classNames?: string[];
    });
    classNames: string[];
    classifyImageUrl: (url: string) => Promise<ClassificationResult[]>;
    classifyImageFile: (file: File) => Promise<ClassificationResult[]>;
    classifyImageTensor: (imageTensor: tf.Tensor3D | tf.Tensor4D) => Promise<ClassificationResult[]>;
    classifyImage: (image: Parameters<typeof tf.browser.fromPixels>[0]) => Promise<ClassificationResult[]>;
    classifyImageEmbedding: (embedding: tf.Tensor) => Promise<ClassificationResult[]>;
    compile: () => void;
    train: (options: tf.ModelFitArgs & {
        x: tf.Tensor<tf.Rank>;
        y: tf.Tensor<tf.Rank>;
        /** @description to calculate classWeight */
        classCounts?: number[];
    }) => Promise<tf.History>;
}>;