Upload classifier

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+clf.skops filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,284 +1,12 @@
----
-library_name: sklearn
-tags:
-- sklearn
-- skops
-- tabular-classification
-model_format: skops
-model_file: clf.skops
-widget:
-- structuredData:
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----
+# detectree model
 
-# Model description
+Pre-trained tree/non-tree pixel classifier for the [detectree](https://github.com/martibosch/detectree) library, trained on aerial imagery of Zurich using the Cluster-I workflow.
 
-LightGBM classifier of tree/non-tree pixels from aerial imagery trained on Zurich's Orthofoto Sommer 2014/15 using detectree.
+## Evaluation metrics
 
-## Intended uses & limitations
-
-Segment tree/non-tree pixels from aerial imagery
-
-## Training Procedure
-
-[More Information Needed]
-
-### Hyperparameters
-
-<details>
-<summary> Click to expand </summary>
-
-| Hyperparameter    | Value   |
-|-------------------|---------|
-| boosting_type     | gbdt    |
-| class_weight      |         |
-| colsample_bytree  | 1.0     |
-| importance_type   | split   |
-| learning_rate     | 0.1     |
-| max_depth         | -1      |
-| min_child_samples | 20      |
-| min_child_weight  | 0.001   |
-| min_split_gain    | 0.0     |
-| n_estimators      | 200     |
-| n_jobs            |         |
-| num_leaves        | 31      |
-| objective         |         |
-| random_state      |         |
-| reg_alpha         | 0.0     |
-| reg_lambda        | 0.0     |
-| subsample         | 1.0     |
-| subsample_for_bin | 200000  |
-| subsample_freq    | 0       |
-
-</details>
-
-### Model Plot
-
-<style>#sk-container-id-15 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: black;--sklearn-color-line: gray;/* Definition of color scheme for unfitted estimators */--sklearn-color-unfitted-level-0: #fff5e6;--sklearn-color-unfitted-level-1: #f6e4d2;--sklearn-color-unfitted-level-2: #ffe0b3;--sklearn-color-unfitted-level-3: chocolate;/* Definition of color scheme for fitted estimators */--sklearn-color-fitted-level-0: #f0f8ff;--sklearn-color-fitted-level-1: #d4ebff;--sklearn-color-fitted-level-2: #b3dbfd;--sklearn-color-fitted-level-3: cornflowerblue;/* Specific color for light theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-icon: #696969;@media (prefers-color-scheme: dark) {/* Redefinition of color scheme for dark theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-icon: #878787;}
-}#sk-container-id-15 {color: var(--sklearn-color-text);
-}#sk-container-id-15 pre {padding: 0;
-}#sk-container-id-15 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;
-}#sk-container-id-15 div.sk-dashed-wrapped {border: 1px dashed var(--sklearn-color-line);margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: var(--sklearn-color-background);
-}#sk-container-id-15 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }`but bootstrap.min.css set `[hidden] { display: none !important; }`so we also need the `!important` here to be able to override thedefault hidden behavior on the sphinx rendered scikit-learn.org.See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;
-}#sk-container-id-15 div.sk-text-repr-fallback {display: none;
-}div.sk-parallel-item,
-div.sk-serial,
-div.sk-item {/* draw centered vertical line to link estimators */background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));background-size: 2px 100%;background-repeat: no-repeat;background-position: center center;
-}/* Parallel-specific style estimator block */#sk-container-id-15 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1;
-}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
-}#sk-container-id-15 div.sk-parallel-item {display: flex;flex-direction: column;
-}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;
-}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
-}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;
-}/* Serial-specific style estimator block */#sk-container-id-15 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em;
-}/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-clickable and can be expanded/collapsed.
-- Pipeline and ColumnTransformer use this feature and define the default style
-- Estimators will overwrite some part of the style using the `sk-estimator` class
-*//* Pipeline and ColumnTransformer style (default) */#sk-container-id-15 div.sk-toggleable {/* Default theme specific background. It is overwritten whether we have aspecific estimator or a Pipeline/ColumnTransformer */background-color: var(--sklearn-color-background);
-}/* Toggleable label */
-#sk-container-id-15 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;
-}#sk-container-id-15 label.sk-toggleable__label-arrow:before {/* Arrow on the left of the label */content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon);
-}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
-}/* Toggleable content - dropdown */#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
-}#sk-container-id-15 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
-}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
-}#sk-container-id-15 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
-}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto;
-}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
-}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
-}#sk-container-id-15 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2);
-}/* Estimator-specific style *//* Colorize estimator box */
-#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
-}#sk-container-id-15 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
-}#sk-container-id-15 div.sk-label label.sk-toggleable__label,
-#sk-container-id-15 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background);
-}/* On hover, darken the color of the background */
-#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
-}/* Label box, darken color on hover, fitted */
-#sk-container-id-15 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2);
-}/* Estimator label */#sk-container-id-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
-}#sk-container-id-15 div.sk-label-container {text-align: center;
-}/* Estimator-specific */
-#sk-container-id-15 div.sk-estimator {font-family: monospace;border: 1px dotted var(--sklearn-color-border-box);border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
-}#sk-container-id-15 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
-}/* on hover */
-#sk-container-id-15 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
-}#sk-container-id-15 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
-}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,
-a:link.sk-estimator-doc-link,
-a:visited.sk-estimator-doc-link {float: right;font-size: smaller;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1em;height: 1em;width: 1em;text-decoration: none !important;margin-left: 1ex;/* unfitted */border: var(--sklearn-color-unfitted-level-1) 1pt solid;color: var(--sklearn-color-unfitted-level-1);
-}.sk-estimator-doc-link.fitted,
-a:link.sk-estimator-doc-link.fitted,
-a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
-}/* On hover */
-div.sk-estimator:hover .sk-estimator-doc-link:hover,
-.sk-estimator-doc-link:hover,
-div.sk-label-container:hover .sk-estimator-doc-link:hover,
-.sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
-}div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
-.sk-estimator-doc-link.fitted:hover,
-div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
-.sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
-}/* Span, style for the box shown on hovering the info icon */
-.sk-estimator-doc-link span {display: none;z-index: 9999;position: relative;font-weight: normal;right: .2ex;padding: .5ex;margin: .5ex;width: min-content;min-width: 20ex;max-width: 50ex;color: var(--sklearn-color-text);box-shadow: 2pt 2pt 4pt #999;/* unfitted */background: var(--sklearn-color-unfitted-level-0);border: .5pt solid var(--sklearn-color-unfitted-level-3);
-}.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3);
-}.sk-estimator-doc-link:hover span {display: block;
-}/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-15 a.estimator_doc_link {float: right;font-size: 1rem;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1rem;height: 1rem;width: 1rem;text-decoration: none;/* unfitted */color: var(--sklearn-color-unfitted-level-1);border: var(--sklearn-color-unfitted-level-1) 1pt solid;
-}#sk-container-id-15 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
-}/* On hover */
-#sk-container-id-15 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
-}#sk-container-id-15 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
-}
-</style><div id="sk-container-id-15" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>LGBMClassifier(n_estimators=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-15" type="checkbox" checked><label for="sk-estimator-id-15" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;LGBMClassifier<span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>LGBMClassifier(n_estimators=200)</pre></div> </div></div></div></div>
-
-## Evaluation Results
-
-Metrics calculated on a validation set of 1% of the test tiles
-
-| Metric    |    Value |
-|-----------|----------|
-| accuracy  | 0.87635  |
-| precision | 0.785237 |
-| recall    | 0.756414 |
-| f1        | 0.770556 |
-
-## Dataset description
-
-https://www.geolion.zh.ch/geodatensatz/2831
-
-## Preprocessing description
-
-Images are resampled to 50 cm resolution. Train/test split based on image descriptors with 1% of tiles selected for training.
-
-# How to Get Started with the Model
-
-[More Information Needed]
-
-# Model Card Authors
-
-Martí Bosch
-
-# Model Card Contact
-
-marti.bosch@epfl.ch
-
-# Citation
-
-https://joss.theoj.org/papers/10.21105/joss.02172
-
-# Example predictions
-
-<details>
-<summary> Click to expand </summary>
-
-![Example predictions](plot.png)
-
-</details>
+| Metric | Value |
+| --- | --- |
+| Accuracy | 0.8557 |
+| Precision | 0.6977 |
+| Recall | 0.8226 |
+| F1 | 0.7550 |