/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
import * as tflite from '@tensorflow/tfjs-tflite';
import { TaskModelLoader } from '../../task_model';
import { Runtime, Task, TFLiteCustomModelCommonLoadingOption } from '../common';
import { ObjectDetectorTFLite } from './tflite_common';
declare type TFLiteNS = typeof tflite;
/** Loading options. */
export interface ODCustomModelTFLiteLoadingOptions extends TFLiteCustomModelCommonLoadingOption, tflite.ObjectDetectorOptions {
}
/**
 * Inference options.
 *
 * TODO: placeholder for now.
 */
export interface ODCustomModelTFLiteInferenceOptions {
}
/** Loader for custom object detection TFLite model. */
export declare class ObjectDetectionCustomModelTFLiteLoader extends TaskModelLoader<TFLiteNS, ODCustomModelTFLiteLoadingOptions, ODCustomModelTFLite> {
    readonly metadata: {
        name: string;
        description: string;
        resourceUrls: {
            'TFLite task library': string;
        };
        runtime: Runtime;
        version: string;
        supportedTasks: Task[];
    };
    readonly packageUrls: string[][];
    readonly sourceModelGlobalNamespace = "tflite";
    protected transformSourceModel(sourceModelGlobal: TFLiteNS, loadingOptions?: ODCustomModelTFLiteLoadingOptions): Promise<ODCustomModelTFLite>;
}
/**
 * A custom TFLite object detection model loaded from a model url or an
 * `ArrayBuffer` in memory.
 *
 * The underlying object detector is built on top of the [TFLite Task
 * Library](https://www.tensorflow.org/lite/inference_with_metadata/task_library/overview).
 * As a result, the custom model needs to meet the [metadata
 * requirements](https://www.tensorflow.org/lite/inference_with_metadata/task_library/object_detector#model_compatibility_requirements).
 *
 * Usage:
 *
 * ```js
 * // Load the model from a custom url with other options (optional).
 * const model = await tfTask.ObjectDetection.CustomModel.TFLite.load({
 *   model:
 * 'https://tfhub.dev/tensorflow/lite-model/ssd_mobilenet_v1/1/metadata/2?lite-format=tflite',
 * });
 *
 * // Run inference on an image.
 * const img = document.querySelector('img');
 * const result = await model.predict(img);
 * console.log(result.objects);
 *
 * // Clean up.
 * model.cleanUp();
 * ```
 *
 * Refer to `tfTask.ObjectDetector` for the `predict` and `cleanUp` method.
 *
 * @docextratypes [
 *   {description: 'Options for `load`', symbol:
 * 'ODCustomModelTFLiteLoadingOptions'},
 *   {description: 'Options for `predict`', symbol:
 * 'ODCustomModelTFLiteInferenceOptions'}
 * ]
 *
 *
 * @doc {heading: 'Object Detection', subheading: 'Models'}
 */
export declare class ODCustomModelTFLite extends ObjectDetectorTFLite<ODCustomModelTFLiteInferenceOptions> {
}
export declare const objectDetectorCustomModelTfliteLoader: ObjectDetectionCustomModelTFLiteLoader;
export {};