Dataset Open Access
Sticky Pi -- Machine Learning Data, Configuration and Models
Quentin Geissmann
MARC21 XML Export
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<subfield code="a"><p><strong>Dataset for the Machine Learning section of the Sticky Pi project (https://doc.sticky-pi.com/)</strong></p>
<p>Contains the dataset for the three algorithms described in the publication: Universal Insect Detector, Siamese Insect Matcher and Insect Tuboid Classifier.</p>
<p><strong>Universal Insect Detector:</strong></p>
<p>`universal_insect_detector/` contains training/validation data, configuration files to train the model, and the model as trained and used for publication.</p>
<ul>
<li>`data/` &ndash; A set of svg images that contain the embedded jpg raw image, and a set of non-intersecting polygon around the labelled insects</li>
<li>`output/`
<ul>
<li>`model_final.pth` &ndash; the model as trained for the publication</li>
</ul>
</li>
<li>`config/`
<ul>
<li>`config.yaml` &ndash; The configuration file defining the hyperparameters to train the model as well as the taxonomic labels</li>
<li>`config.yaml `&ndash; The configuration file defining the hyperparameters to train the model</li>
<li>`mask_rcnn_R_101_C4_3x.yaml` &ndash; the base configuration file from which config is derived</li>
</ul>
</li>
</ul>
<p>&nbsp;</p>
<p><strong>Siamese Insect Matcher</strong></p>
<p>`siamese_insect_matcher/` contains training/validation data, configuration files to train the model, and the model as trained and used for publication.</p>
<ul>
<li>`data/` &ndash; a set of svg images that contain two embedded jpg raw images vertically stacked corresponding to two frames in a series. Each predicted insect is labelled as a polygon. Insects that are labelled as the same instance, between the two frames, are grouped (i.e. SVG group). The filename of each image is `&lt;device&gt;.&lt;datetime_frame_1&gt;.&lt;datetime_frame_2&gt;.svg`</li>
<li>`output/`
<ul>
<li>`model_final.pth` &ndash; the model as trained for the publication</li>
</ul>
</li>
<li>`config/`
<ul>
<li>`config.yaml` &ndash; The configuration file defining the hyperparameters to train the model as well as the taxonomic labels</li>
<li>`config.yaml` &ndash; The configuration file defining the hyperparameters to train the model</li>
</ul>
</li>
</ul>
<p>&nbsp;</p>
<p><strong>Insect Tuboid Classifier:</strong></p>
<p>`insect_tuboid_classifier/` contains images of insect tuboid, a database file describing their taxonomy, a configuration file to train the model, and the model as trained and used for publication.</p>
<ul>
<li>`data/`
<ul>
<li>`database.db`: a sqlite file with a single table `ANNOTATIONS`. The table maps a unique identifier of each tuboid (tuboid_id) to a set of manually annotated taxonomic variables.</li>
<li>A directory tree of the form: `&lt;series_id&gt;/&lt;tuboid_id&gt;/`. Each terminal directory contains:
<ul>
<li>
<ul>
<li>`tuboid.jpg` &ndash; a jpeg image made of 224 x 224 tiles representing all the shots in a tuboid, left to right, top to bottom &ndash; might be padded with empty images</li>
<li>`metadata.txt` &ndash; a csv text file with columns:
<ul>
<li>
<ul>
<li>parrent_image_id &ndash; &lt;device&gt;.&lt;UTC_datetime&gt;</li>
<li>X &ndash; the X coordinates of the object centroid</li>
<li>Y &ndash; the Y coordinates of the object centroid</li>
</ul>
</li>
</ul>
</li>
<li>scale &ndash; The scaling factor applied between the original and image and the 224 x 224 tile (&gt;1 =&gt; image was enlarged)</li>
<li>`context.jpg` &ndash; a representation of the first whole image of a series, with a box around the first tuboid shot (this is for debugging/labelling purposes)</li>
</ul>
</li>
</ul>
</li>
</ul>
</li>
<li>`output/`
<ul>
<li>`model_final.pth` &ndash; the model as trained for the publication</li>
</ul>
</li>
<li>config/
<ul>
<li>`config.yaml` &ndash; The configuration file defining the hyperparameters to train the model as well as the taxonomic labels</li>
</ul>
</li>
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| Unique downloads | 162 | 38 |
Indexed in
- Publication date:
- April 12, 2021
- DOI:
-
- Keyword(s):
- instect traps behavioral ecology
- License (for files):
- Creative Commons Attribution 4.0 International