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Docs: Update links to PDF documents on dl.photoprism.app

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Signed-off-by: Michael Mayer <michael@photoprism.app>
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Michael Mayer
2024-07-19 18:13:08 +02:00
parent 1140ceea40
commit 02234e7c06
3 changed files with 3 additions and 3 deletions
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2
internal/commands/show_config_options.go
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@@ -19,7 +19,7 @@ var ShowConfigOptionsCommand = cli.Command{
}
var faceFlagsInfo = `!!! info ""
To [recognize faces](https://docs.photoprism.app/user-guide/organize/people/), PhotoPrism first extracts crops from your images using a [library](https://github.com/esimov/pigo) based on [pixel intensity comparisons](https://dl.photoprism.app/pdf/20140820-Pixel_Intensity_Comparisons.pdf). These are then fed into TensorFlow to compute [512-dimensional vectors](https://dl.photoprism.app/pdf/20150101-FaceNet.pdf) for characterization. In the final step, the [DBSCAN algorithm](https://en.wikipedia.org/wiki/DBSCAN) attempts to cluster these so-called face embeddings, so they can be matched to persons with just a few clicks. A reasonable range for the similarity distance between face embeddings is between 0.60 and 0.70, with a higher value being more aggressive and leading to larger clusters with more false positives. To cluster a smaller number of faces, you can reduce the core to 3 or 2 similar faces.
To [recognize faces](https://docs.photoprism.app/user-guide/organize/people/), PhotoPrism first extracts crops from your images using a [library](https://github.com/esimov/pigo) based on [pixel intensity comparisons](https://dl.photoprism.app/pdf/publications/20140820-Pixel_Intensity_Comparisons.pdf). These are then fed into TensorFlow to compute [512-dimensional vectors](https://dl.photoprism.app/pdf/publications/20150101-FaceNet.pdf) for characterization. In the final step, the [DBSCAN algorithm](https://en.wikipedia.org/wiki/DBSCAN) attempts to cluster these so-called face embeddings, so they can be matched to persons with just a few clicks. A reasonable range for the similarity distance between face embeddings is between 0.60 and 0.70, with a higher value being more aggressive and leading to larger clusters with more false positives. To cluster a smaller number of faces, you can reduce the core to 3 or 2 similar faces.
We recommend that only advanced users change these parameters:`

2
internal/config/report_sections.go
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@@ -8,7 +8,7 @@ type ReportSection struct {
}
var faceFlagsInfo = `!!! info ""
To [recognize faces](https://docs.photoprism.app/user-guide/organize/people/), PhotoPrism first extracts crops from your images using a [library](https://github.com/esimov/pigo) based on [pixel intensity comparisons](https://dl.photoprism.app/pdf/20140820-Pixel_Intensity_Comparisons.pdf). These are then fed into TensorFlow to compute [512-dimensional vectors](https://dl.photoprism.app/pdf/20150101-FaceNet.pdf) for characterization. In the final step, the [DBSCAN algorithm](https://en.wikipedia.org/wiki/DBSCAN) attempts to cluster these so-called face embeddings, so they can be matched to persons with just a few clicks. A reasonable range for the similarity distance between face embeddings is between 0.60 and 0.70, with a higher value being more aggressive and leading to larger clusters with more false positives. To cluster a smaller number of faces, you can reduce the core to 3 or 2 similar faces.
To [recognize faces](https://docs.photoprism.app/user-guide/organize/people/), PhotoPrism first extracts crops from your images using a [library](https://github.com/esimov/pigo) based on [pixel intensity comparisons](https://dl.photoprism.app/pdf/publications/20140820-Pixel_Intensity_Comparisons.pdf). These are then fed into TensorFlow to compute [512-dimensional vectors](https://dl.photoprism.app/pdf/publications/20150101-FaceNet.pdf) for characterization. In the final step, the [DBSCAN algorithm](https://en.wikipedia.org/wiki/DBSCAN) attempts to cluster these so-called face embeddings, so they can be matched to persons with just a few clicks. A reasonable range for the similarity distance between face embeddings is between 0.60 and 0.70, with a higher value being more aggressive and leading to larger clusters with more false positives. To cluster a smaller number of faces, you can reduce the core to 3 or 2 similar faces.
We recommend that only advanced users change these parameters:`

2
pkg/clusters/README.md
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@@ -98,7 +98,7 @@ finish <- struct{}{}
fmt.Printf("Clustered data set into %d\n", c.Sizes())
```
The Estimator interface defines an operation of guessing an optimal number of clusters in a dataset. As of now the KMeansEstimator is implemented using gap statistic and k-means++ as the clustering algorithm (see https://dl.photoprism.app/pdf/20020106-Estimating_the_Number_of_Clusters.pdf):
The Estimator interface defines an operation of guessing an optimal number of clusters in a dataset. As of now the KMeansEstimator is implemented using gap statistic and k-means++ as the clustering algorithm (see https://dl.photoprism.app/pdf/publications/20020106-Estimating_the_Number_of_Clusters.pdf):
```go
var data [][]float64