54 lines
2.1 KiB
Protocol Buffer
54 lines
2.1 KiB
Protocol Buffer
/* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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syntax = "proto2";
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package mediapipe.tasks.components.containers.proto;
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import "mediapipe/framework/formats/classification.proto";
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option java_package = "com.google.mediapipe.tasks.components.containers.proto";
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option java_outer_classname = "ClassificationsProto";
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// Classifications for a given classifier head, i.e. for a given output tensor.
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message Classifications {
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// The classification results for this head.
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optional mediapipe.ClassificationList classification_list = 4;
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// The index of the classifier head these categories refer to. This is useful
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// for multi-head models.
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optional int32 head_index = 2;
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// The name of the classifier head, which is the corresponding tensor metadata
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// name.
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// TODO: Add github link to metadata_schema.fbs.
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optional string head_name = 3;
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// Reserved fields.
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reserved 1;
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}
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// Classifications for a given classifier model.
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message ClassificationResult {
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// The classification results for each model head, i.e. one for each output
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// tensor.
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repeated Classifications classifications = 1;
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// The optional timestamp (in milliseconds) of the start of the chunk of data
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// corresponding to these results.
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//
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// This is only used for classification on time series (e.g. audio
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// classification). In these use cases, the amount of data to process might
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// exceed the maximum size that the model can process: to solve this, the
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// input data is split into multiple chunks starting at different timestamps.
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optional int64 timestamp_ms = 2;
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}
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