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Reformat Markdown code blocks (#12795)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-05-18 18:58:06 +02:00 • committed by GitHub
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48
docs/en/datasets/explorer/api.md
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@ -36,13 +36,13 @@ pip install ultralytics[explorer]
from ultralytics import Explorer
# Create an Explorer object
explorer = Explorer(data='coco128.yaml', model='yolov8n.pt')
explorer = Explorer(data="coco128.yaml", model="yolov8n.pt")
# Create embeddings for your dataset
explorer.create_embeddings_table()
# Search for similar images to a given image/images
dataframe = explorer.get_similar(img='path/to/image.jpg')
dataframe = explorer.get_similar(img="path/to/image.jpg")
# Or search for similar images to a given index/indices
dataframe = explorer.get_similar(idx=0)
@ -75,18 +75,17 @@ You get a pandas dataframe with the `limit` number of most similar data points t
from ultralytics import Explorer
# create an Explorer object
exp = Explorer(data='coco128.yaml', model='yolov8n.pt')
exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
exp.create_embeddings_table()
similar = exp.get_similar(img='https://ultralytics.com/images/bus.jpg', limit=10)
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
)
img=["https://ultralytics.com/images/bus.jpg", "https://ultralytics.com/images/bus.jpg"],
limit=10,
)
print(similar.head())
```
@ -96,14 +95,14 @@ You get a pandas dataframe with the `limit` number of most similar data points t
from ultralytics import Explorer
# create an Explorer object
exp = Explorer(data='coco128.yaml', model='yolov8n.pt')
exp = Explorer(data="coco128.yaml", model="yolov8n.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)
similar = exp.get_similar(idx=[1, 10], limit=10)
print(similar.head())
```
@ -119,10 +118,10 @@ You can also plot the similar images using the `plot_similar` method. This metho
from ultralytics import Explorer
# create an Explorer object
exp = Explorer(data='coco128.yaml', model='yolov8n.pt')
exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
exp.create_embeddings_table()
plt = exp.plot_similar(img='https://ultralytics.com/images/bus.jpg', limit=10)
plt = exp.plot_similar(img="https://ultralytics.com/images/bus.jpg", limit=10)
plt.show()
```
@ -132,7 +131,7 @@ You can also plot the similar images using the `plot_similar` method. This metho
from ultralytics import Explorer
# create an Explorer object
exp = Explorer(data='coco128.yaml', model='yolov8n.pt')
exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
exp.create_embeddings_table()
plt = exp.plot_similar(idx=1, limit=10)
@ -150,9 +149,8 @@ Note: This works using LLMs under the hood so the results are probabilistic and
from ultralytics import Explorer
from ultralytics.data.explorer import plot_query_result
# create an Explorer object
exp = Explorer(data='coco128.yaml', model='yolov8n.pt')
exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
exp.create_embeddings_table()
df = exp.ask_ai("show me 100 images with exactly one person and 2 dogs. There can be other objects too")
@ -173,7 +171,7 @@ You can run SQL queries on your dataset using the `sql_query` method. This metho
from ultralytics import Explorer
# create an Explorer object
exp = Explorer(data='coco128.yaml', model='yolov8n.pt')
exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
exp.create_embeddings_table()
df = exp.sql_query("WHERE labels LIKE '%person%' AND labels LIKE '%dog%'")
@ -190,7 +188,7 @@ You can also plot the results of a SQL query using the `plot_sql_query` method.
from ultralytics import Explorer
# create an Explorer object
exp = Explorer(data='coco128.yaml', model='yolov8n.pt')
exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
exp.create_embeddings_table()
# plot the SQL Query
@ -293,7 +291,7 @@ You can use similarity index to build custom conditions to filter out the datase
import numpy as np
sim_count = np.array(sim_idx["count"])
sim_idx['im_file'][sim_count > 30]
sim_idx["im_file"][sim_count > 30]
```
### Visualize Embedding Space
@ -301,10 +299,10 @@ sim_idx['im_file'][sim_count > 30]
You can also visualize the embedding space using the plotting tool of your choice. For example here is a simple example using matplotlib:
```python
import numpy as np
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from sklearn.decomposition import PCA
# Reduce dimensions using PCA to 3 components for visualization in 3D
pca = PCA(n_components=3)
@ -312,14 +310,14 @@ reduced_data = pca.fit_transform(embeddings)
# Create a 3D scatter plot using Matplotlib Axes3D
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111, projection='3d')
ax = fig.add_subplot(111, projection="3d")
# Scatter plot
ax.scatter(reduced_data[:, 0], reduced_data[:, 1], reduced_data[:, 2], alpha=0.5)
ax.set_title('3D Scatter Plot of Reduced 256-Dimensional Data (PCA)')
ax.set_xlabel('Component 1')
ax.set_ylabel('Component 2')
ax.set_zlabel('Component 3')
ax.set_title("3D Scatter Plot of Reduced 256-Dimensional Data (PCA)")
ax.set_xlabel("Component 1")
ax.set_ylabel("Component 2")
ax.set_zlabel("Component 3")
plt.show()
```