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Update to lowercase MkDocs admonitions (#15990)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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24
docs/en/datasets/explorer/api.md
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@ -48,7 +48,7 @@ dataframe = explorer.get_similar(img="path/to/image.jpg")
dataframe = explorer.get_similar(idx=0)
```
!!! Tip "Note"
!!! note
Embeddings table for a given dataset and model pair is only created once and reused. These use [LanceDB](https://lancedb.github.io/lancedb/) under the hood, which scales on-disk, so you can create and reuse embeddings for large datasets like COCO without running out of memory.
@ -67,7 +67,7 @@ In case of multiple inputs, the aggregate of their embeddings is used.
You get a pandas dataframe with the `limit` number of most similar data points to the input, along with their distance in the embedding space. You can use this dataset to perform further filtering
!!! Example "Semantic Search"
!!! example "Semantic Search"
=== "Using Images"
@ -110,7 +110,7 @@ You get a pandas dataframe with the `limit` number of most similar data points t
You can also plot the similar images using the `plot_similar` method. This method takes the same arguments as `get_similar` and plots the similar images in a grid.
!!! Example "Plotting Similar Images"
!!! example "Plotting Similar Images"
=== "Using Images"
@ -143,7 +143,7 @@ You can also plot the similar images using the `plot_similar` method. This metho
This allows you to write how you want to filter your dataset using natural language. You don't have to be proficient in writing SQL queries. Our AI powered query generator will automatically do that under the hood. For example - you can say - "show me 100 images with exactly one person and 2 dogs. There can be other objects too" and it'll internally generate the query and show you those results.
Note: This works using LLMs under the hood so the results are probabilistic and might get things wrong sometimes
!!! Example "Ask AI"
!!! example "Ask AI"
```python
from ultralytics import Explorer
@ -165,7 +165,7 @@ Note: This works using LLMs under the hood so the results are probabilistic and
You can run SQL queries on your dataset using the `sql_query` method. This method takes a SQL query as input and returns a pandas dataframe with the results.
!!! Example "SQL Query"
!!! example "SQL Query"
```python
from ultralytics import Explorer
@ -182,7 +182,7 @@ You can run SQL queries on your dataset using the `sql_query` method. This metho
You can also plot the results of a SQL query using the `plot_sql_query` method. This method takes the same arguments as `sql_query` and plots the results in a grid.
!!! Example "Plotting SQL Query Results"
!!! example "Plotting SQL Query Results"
```python
from ultralytics import Explorer
@ -199,7 +199,9 @@ You can also plot the results of a SQL query using the `plot_sql_query` method.
You can also work with the embeddings table directly. Once the embeddings table is created, you can access it using the `Explorer.table`
!!! Tip "Explorer works on [LanceDB](https://lancedb.github.io/lancedb/) tables internally. You can access this table directly, using `Explorer.table` object and run raw queries, push down pre- and post-filters, etc."
!!! tip
Explorer works on [LanceDB](https://lancedb.github.io/lancedb/) tables internally. You can access this table directly, using `Explorer.table` object and run raw queries, push down pre- and post-filters, etc.
```python
from ultralytics import Explorer
@ -213,7 +215,7 @@ Here are some examples of what you can do with the table:
### Get raw Embeddings
!!! Example
!!! example
```python
from ultralytics import Explorer
@ -228,7 +230,7 @@ Here are some examples of what you can do with the table:
### Advanced Querying with pre- and post-filters
!!! Example
!!! example
```python
from ultralytics import Explorer
@ -270,11 +272,11 @@ It returns a pandas dataframe with the following columns:
- `count`: Number of images in the dataset that are closer than `max_dist` to the current image
- `sim_im_files`: List of paths to the `count` similar images
!!! Tip
!!! tip
For a given dataset, model, `max_dist` & `top_k` the similarity index once generated will be reused. In case, your dataset has changed, or you simply need to regenerate the similarity index, you can pass `force=True`.
!!! Example "Similarity Index"
!!! example "Similarity Index"
```python
from ultralytics import Explorer