Struct google_api_proto::google::cloud::datalabeling::v1beta1::pr_curve::ConfidenceMetricsEntry

pub struct ConfidenceMetricsEntry {
    pub confidence_threshold: f32,
    pub recall: f32,
    pub precision: f32,
    pub f1_score: f32,
    pub recall_at1: f32,
    pub precision_at1: f32,
    pub f1_score_at1: f32,
    pub recall_at5: f32,
    pub precision_at5: f32,
    pub f1_score_at5: f32,
}

Fields

confidence_threshold: f32

Threshold used for this entry.

For classification tasks, this is a classification threshold: a predicted label is categorized as positive or negative (in the context of this point on the PR curve) based on whether the label’s score meets this threshold.

For image object detection (bounding box) tasks, this is the [intersection-over-union

(IOU)](/vision/automl/object-detection/docs/evaluate#intersection-over-union) threshold for the context of this point on the PR curve.

recall: f32

Recall value.

precision: f32

Precision value.

f1_score: f32

Harmonic mean of recall and precision.

recall_at1: f32

Recall value for entries with label that has highest score.

precision_at1: f32

Precision value for entries with label that has highest score.

f1_score_at1: f32

The harmonic mean of [recall_at1][google.cloud.datalabeling.v1beta1.PrCurve.ConfidenceMetricsEntry.recall_at1] and [precision_at1][google.cloud.datalabeling.v1beta1.PrCurve.ConfidenceMetricsEntry.precision_at1].

recall_at5: f32

Recall value for entries with label that has highest 5 scores.

precision_at5: f32

Precision value for entries with label that has highest 5 scores.

f1_score_at5: f32

The harmonic mean of [recall_at5][google.cloud.datalabeling.v1beta1.PrCurve.ConfidenceMetricsEntry.recall_at5] and [precision_at5][google.cloud.datalabeling.v1beta1.PrCurve.ConfidenceMetricsEntry.precision_at5].

Trait Implementations

impl Clone for ConfidenceMetricsEntry

fn clone(&self) -> ConfidenceMetricsEntry

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ConfidenceMetricsEntry

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for ConfidenceMetricsEntry

fn default() -> Self

Returns the “default value” for a type.

impl Message for ConfidenceMetricsEntry

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode(&self, buf: &mut impl BufMut) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

fn encode_length_delimited( &self, buf: &mut impl BufMut, ) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message with a length-delimiter to a buffer.

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message with a length-delimiter to a newly allocated buffer.

fn decode(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes a length-delimited instance of the message from the buffer.

fn merge(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq for ConfidenceMetricsEntry

fn eq(&self, other: &ConfidenceMetricsEntry) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Copy for ConfidenceMetricsEntry

impl StructuralPartialEq for ConfidenceMetricsEntry