How to do Object Detection in Kolena - Task Doc Template (#583)

* create new OD task doc

* fix 404s

* stash

* update to new links and sync with new docs

* proofread

* Simplify content and mention util functions

* Fix links and special char

* small edits

* nits

---------

Co-authored-by: mkaramlou <152192084+mkaramlou@users.noreply.github.com>

docs/automations/set-up-natural-language-search.md

@@ -14,7 +14,7 @@ or manually extracting and uploading corresponding search embeddings using a Kol
 ??? "Requirements"
     - This feature is currently supported for Amazon S3 integrations.
     - Kolena requires access to the content of your images.
-    Read [Connecting Cloud Storage: Amazon S3](../connecting-cloud-storage/) for more details.
+    Read [Connecting Cloud Storage: Amazon S3](../connecting-cloud-storage/amazon-s3.md) for more details.
     - Only account administrators are able to change this setting.
 
 Embedding extractions allow you to find datapoints using natural language or similarity between desired datapoints.
@@ -128,7 +128,7 @@ Once embeddings are extracted for each `locator` on the dataset, we create a dat
 the embeddings.
 
 The dataframe uploaded is required to contain the ID columns of the dataset in order to
-match against the [datapoints](../core-concepts/dataset.md/#datapoints) in the dataset.
+match against the [datapoints](../dataset/core-concepts/index.md#datapoints) in the dataset.
 In this example, the ID column of the dataset is `locator`.
 
 ```{.python .no-copy}

docs/dataset/advanced-usage/dataset-formatting/audio.md

@@ -11,7 +11,8 @@ problems.
 
 ### Supported File Data Formats
 
-The Kolena SDK supports upload of data in the Pandas [`DataFrame`](https://pandas.pydata.org/docs/reference/frame.html) format.
+The Kolena SDK supports uploading of data in the Pandas
+[`DataFrame`](https://pandas.pydata.org/docs/reference/frame.html) format.
 
 The Kolena web app supports the following file formats.
 
@@ -24,10 +25,10 @@ The Kolena web app supports the following file formats.
 ### Using the `locator`
 
 Kolena uses references to files stored in your cloud storage to render them.
-Refer to ["Connecting Cloud Storage"](../../../connecting-cloud-storage/)
+Refer to ["Connecting Cloud Storage"](../../../connecting-cloud-storage/index.md)
 for details on how to configure this.
 
-Audio samples can be visualized on Kolena one of two ways.
+Audio samples can be visualized on Kolena in one of two ways.
 
 **Gallery mode**: visualizes each audio as a tile.
 To enable the Gallery view store references to audio files in a column named `locator`. `locator` can be used as

docs/dataset/advanced-usage/dataset-formatting/computer-vision.md

@@ -10,7 +10,8 @@ problems.
 
 ### Supported File Data Formats
 
-The Kolena SDK supports upload of data in the Pandas [`DataFrame`](https://pandas.pydata.org/docs/reference/frame.html) format.
+The Kolena SDK supports uploading of data in the Pandas
+[`DataFrame`](https://pandas.pydata.org/docs/reference/frame.html) format.
 
 The Kolena web app supports the following file formats.
 
@@ -31,7 +32,7 @@ Supported file types are:
 ### Using the `locator`
 
 Kolena uses references to files stored in your cloud storage to render them.
-Refer to ["Connecting Cloud Storage"](../../../connecting-cloud-storage/)
+Refer to ["Connecting Cloud Storage"](../../../connecting-cloud-storage/index.md)
 for details on how to configure this.
 
 Computer Vision data is best visualized in Studio using the Gallery mode.
@@ -196,7 +197,7 @@ To render 3D Bounding boxes you can use
 
     If you wish to analyze your model results based on specific characteristics of your bounding boxes
     you can provide values representing those characteristics using additional key value pairs.
-    For example if location of a bounding box is important you can construct your `LabeledBoundingBox3D` like this
+    For example, if location of a bounding box is important you can construct your `LabeledBoundingBox3D` like this
     ```python
     LabeledBoundingBox3D(
         center=(313.02, 12.01, 15.5),

docs/dataset/advanced-usage/dataset-formatting/natural-language.md

@@ -10,7 +10,8 @@ problems.
 
 ### Supported File Data Formats
 
-The Kolena SDK supports upload of data in the Pandas [`DataFrame`](https://pandas.pydata.org/docs/reference/frame.html) format.
+The Kolena SDK supports uploading of data in the Pandas
+[`DataFrame`](https://pandas.pydata.org/docs/reference/frame.html) format.
 
 The Kolena web app supports the following file formats.
 
@@ -41,11 +42,11 @@ To enable this view, include your primary text values in your `.CSV` in a column
 !!! tip
     Use the [`TextSegment`](../../../reference/annotation.md#kolena.annotation.TextSegment) or
     [`LabeledTextSegment`](../../../reference/annotation.md#kolena.annotation.LabeledTextSegment) annotations
-    to highlights parts of your text that is of interest to you.
+    to highlight parts of your text that is of interest to you.
 
 **Tabular mode**: visualizes each text field with its corresponding meta-data in a table with common table functionalities.
 
-To use this view, simply provide the text values in your `.CSV` in any column named other than `text`.
+To use this view, simply provide the text values in your `.CSV` with any column names other than `text`.
 <figure markdown>
 ![Tabular View](../../../assets/images/tabular-text-dark.png#only-dark)
 ![Tabular View](../../../assets/images/tabular-text-light.png#only-light)

docs/dataset/advanced-usage/quality-standard-results.md

@@ -23,8 +23,8 @@ The return value is a multi-index DataFrame with indices `(stratification, test_
 metric_group, metric)`.
 
 <figure markdown>
-![Quality Standard Result](../../assets/images/‎quality_standard_diagram_light.jpeg#only-light)
-![Quality Standard Result](../../assets/images/‎quality_standard_diagram_dark.jpeg#only-dark)
+![Quality Standard Result](../../assets/images/quality_standard_diagram_light.jpeg#only-light)
+![Quality Standard Result](../../assets/images/quality_standard_diagram_dark.jpeg#only-dark)
 <figcaption>Quality Standard Result</figcaption>
 </figure>
 
@@ -33,7 +33,7 @@ metric_group, metric)`.
 In order to automate deployment decisions with Kolena a team could:
 
 1. Define the metric requirements a model must meet in order to be considered for deployment.
-2. [Upload model results](../../reference/dataset/index.md##kolena.dataset.evaluation.upload_results) as part of a CI/CD
+2. [Upload model results](../../reference/dataset/index.md#kolena.dataset.evaluation.upload_results) as part of a CI/CD
    pipeline.
 3. [Download the dataset's quality standard
    results](../../reference/experimental/index.md#kolena._experimental.quality_standard.download_quality_standard_result)