import { BatchNorm2d, Conv2d, type Layer, Tensor } from '@jsgrad/jsgrad/base';
export declare const get_variant_multiples: (variant: string) => number[] | undefined;
declare class Upsample {
    scale_factor: number;
    mode: 'nearest';
    constructor(scale_factor: number, mode?: 'nearest');
    call: (x: Tensor) => Tensor;
}
declare class Conv_Block {
    conv: Conv2d;
    bn: BatchNorm2d;
    constructor(c1: number, c2: number, kernel_size?: number | number[], stride?: number, groups?: number, dilation?: number, padding?: number);
    call: (x: Tensor) => Tensor;
}
declare class Bottleneck {
    cv1: Conv_Block;
    cv2: Conv_Block;
    residual: boolean;
    constructor(c1: number, c2: number, shortcut: boolean, g?: number, kernels?: number[] | number[][], channel_factor?: number);
    call: (x: Tensor) => Tensor;
}
declare class C2f {
    c: number;
    cv1: Conv_Block;
    cv2: Conv_Block;
    bottleneck: Bottleneck[];
    constructor(c1: number, c2: number, n?: number, shortcut?: boolean, g?: number, e?: number);
    call: (x: Tensor) => Tensor;
}
declare class DFL {
    c1: number;
    conv: Conv2d;
    constructor(c1?: number);
    call: (x: Tensor) => Tensor;
}
declare class Darknet {
    b1: Layer[];
    b2: Layer[];
    b3: Layer[];
    b4: Layer[];
    b5: Layer[];
    constructor(w: number, r: number, d: number);
    return_modules: () => Layer[];
    call: (x: Tensor) => [Tensor, Tensor, Tensor];
}
declare class Yolov8NECK {
    up: Upsample;
    n1: C2f;
    n2: C2f;
    n3: Conv_Block;
    n4: C2f;
    n5: Conv_Block;
    n6: C2f;
    constructor(w: number, r: number, d: number);
    return_modules: () => (Conv_Block | C2f)[];
    call: (p3: Tensor, p4: Tensor, p5: Tensor) => [Tensor, Tensor, Tensor];
}
declare class DetectionHead {
    nc: number;
    ch: number;
    nl: number;
    no: number;
    stride: number[];
    dfl: DFL;
    cv3: Layer[][];
    cv2: Layer[][];
    constructor(nc?: number, filters?: number[]);
    call: (x: Tensor[]) => Tensor;
}
export declare class YOLOv8 {
    net: Darknet;
    fpn: Yolov8NECK;
    head: DetectionHead;
    constructor(w: number, r: number, d: number, num_classes: number);
    call: (x: Tensor) => Tensor;
    return_all_trainable_modules: () => ([number, Layer] | (number | DetectionHead)[])[];
}
export declare const get_weights_location: (yolo_variant: string) => Promise<string>;
export {};