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Remove explorer Integration (#16842)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2024-10-13 20:00:23 +05:00 • committed by GitHub
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docs/en/usage/python.md
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@ -256,50 +256,6 @@ Benchmark mode is used to profile the speed and accuracy of various export forma
[Benchmark Examples](../modes/benchmark.md){ .md-button }
## Explorer
Explorer API can be used to explore datasets with advanced semantic, vector-similarity and SQL search among other features. It also enabled searching for images based on their content using natural language by utilizing the power of LLMs. The Explorer API allows you to write your own dataset exploration notebooks or scripts to get insights into your datasets.
!!! example "Semantic Search Using Explorer"
=== "Using Images"
```python
from ultralytics import Explorer
# create an Explorer object
exp = Explorer(data="coco8.yaml", model="yolo11n.pt")
exp.create_embeddings_table()
similar = exp.get_similar(img="https://ultralytics.com/images/bus.jpg", limit=10)
print(similar.head())
# Search using multiple indices
similar = exp.get_similar(
img=["https://ultralytics.com/images/bus.jpg", "https://ultralytics.com/images/bus.jpg"], limit=10
)
print(similar.head())
```
=== "Using Dataset Indices"
```python
from ultralytics import Explorer
# create an Explorer object
exp = Explorer(data="coco8.yaml", model="yolo11n.pt")
exp.create_embeddings_table()
similar = exp.get_similar(idx=1, limit=10)
print(similar.head())
# Search using multiple indices
similar = exp.get_similar(idx=[1, 10], limit=10)
print(similar.head())
```
[Explorer](../datasets/explorer/index.md){ .md-button }
## Using Trainers
`YOLO` model class is a high-level wrapper on the Trainer classes. Each YOLO task has its own trainer that inherits from `BaseTrainer`.

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docs/en/usage/simple-utilities.md
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@ -25,10 +25,6 @@ The `ultralytics` package comes with a myriad of utilities that can support, enh
## Data
### YOLO Data Explorer
[YOLO Explorer](../datasets/explorer/index.md) was added in the `8.1.0` anniversary update and is a powerful tool you can use to better understand your dataset. One of the key functions that YOLO Explorer provides, is the ability to use text queries to find object instances in your dataset.
### Auto Labeling / Annotations
Dataset annotation is a very resource intensive and time-consuming process. If you have a YOLO [object detection](https://www.ultralytics.com/glossary/object-detection) model trained on a reasonable amount of data, you can use it and [SAM](../models/sam.md) to auto-annotate additional data (segmentation format).