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Ultralytics Explorer API

Introduction

Open In Colab The Explorer API is a Python API for exploring your datasets. It supports filtering and searching your dataset using SQL queries, vector similarity search and semantic search.



Watch: Ultralytics Explorer API Overview

Installation

Explorer depends on external libraries for some of its functionality. These are automatically installed on usage. To manually install these dependencies, use the following command:

pip install ultralytics[explorer]

Usage

from ultralytics import Explorer

# Create an Explorer object
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")

# Or search for similar images to a given index/indices
dataframe = explorer.get_similar(idx=0)

Note

Embeddings table for a given dataset and model pair is only created once and reused. These use 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.

In case you want to force update the embeddings table, you can pass force=True to create_embeddings_table method.

You can directly access the LanceDB table object to perform advanced analysis. Learn more about it in the Working with Embeddings Table section

Similarity search is a technique for finding similar images to a given image. It is based on the idea that similar images will have similar embeddings. Once the embeddings table is built, you can get run semantic search in any of the following ways:

  • On a given index or list of indices in the dataset: exp.get_similar(idx=[1,10], limit=10)
  • On any image or list of images not in the dataset: exp.get_similar(img=["path/to/img1", "path/to/img2"], limit=10)

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

Semantic Search

from ultralytics import Explorer

# create an Explorer object
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)
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())
from ultralytics import Explorer

# create an Explorer object
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)
print(similar.head())

Plotting Similar Images

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.

Plotting Similar Images

from ultralytics import Explorer

# create an Explorer object
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.show()
from ultralytics import Explorer

# create an Explorer object
exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
exp.create_embeddings_table()

plt = exp.plot_similar(idx=1, limit=10)
plt.show()

2. Ask AI (Natural Language Querying)

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

Ask AI

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.create_embeddings_table()

df = exp.ask_ai("show me 100 images with exactly one person and 2 dogs. There can be other objects too")
print(df.head())

# plot the results
plt = plot_query_result(df)
plt.show()

3. SQL Querying

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.

SQL Query

from ultralytics import Explorer

# create an Explorer object
exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
exp.create_embeddings_table()

df = exp.sql_query("WHERE labels LIKE '%person%' AND labels LIKE '%dog%'")
print(df.head())

Plotting SQL Query Results

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.

Plotting SQL Query Results

from ultralytics import Explorer

# create an Explorer object
exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
exp.create_embeddings_table()

# plot the SQL Query
exp.plot_sql_query("WHERE labels LIKE '%person%' AND labels LIKE '%dog%' LIMIT 10")

4. Working with Embeddings Table

You can also work with the embeddings table directly. Once the embeddings table is created, you can access it using the Explorer.table

Explorer works on LanceDB tables internally. You can access this table directly, using Explorer.table object and run raw queries, push down pre- and post-filters, etc.

from ultralytics import Explorer

exp = Explorer()
exp.create_embeddings_table()
table = exp.table

Here are some examples of what you can do with the table:

Get raw Embeddings

Example

from ultralytics import Explorer

exp = Explorer()
exp.create_embeddings_table()
table = exp.table

embeddings = table.to_pandas()["vector"]
print(embeddings)

Advanced Querying with pre- and post-filters

Example

from ultralytics import Explorer

exp = Explorer(model="yolov8n.pt")
exp.create_embeddings_table()
table = exp.table

# Dummy embedding
embedding = [i for i in range(256)]
rs = table.search(embedding).metric("cosine").where("").limit(10)

Create Vector Index

When using large datasets, you can also create a dedicated vector index for faster querying. This is done using the create_index method on LanceDB table.

table.create_index(num_partitions=..., num_sub_vectors=...)

Find more details on the type vector indices available and parameters here In the future, we will add support for creating vector indices directly from Explorer API.

5. Embeddings Applications

You can use the embeddings table to perform a variety of exploratory analysis. Here are some examples:

Similarity Index

Explorer comes with a similarity_index operation:

  • It tries to estimate how similar each data point is with the rest of the dataset.
  • It does that by counting how many image embeddings lie closer than max_dist to the current image in the generated embedding space, considering top_k similar images at a time.

It returns a pandas dataframe with the following columns:

  • idx: Index of the image in the dataset
  • im_file: Path to the image file
  • 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

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.

Similarity Index

from ultralytics import Explorer

exp = Explorer()
exp.create_embeddings_table()

sim_idx = exp.similarity_index()

You can use similarity index to build custom conditions to filter out the dataset. For example, you can filter out images that are not similar to any other image in the dataset using the following code:

import numpy as np

sim_count = np.array(sim_idx["count"])
sim_idx["im_file"][sim_count > 30]

Visualize Embedding Space

You can also visualize the embedding space using the plotting tool of your choice. For example here is a simple example using matplotlib:

import matplotlib.pyplot as plt
from sklearn.decomposition import PCA

# Reduce dimensions using PCA to 3 components for visualization in 3D
pca = PCA(n_components=3)
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")

# 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")

plt.show()

Start creating your own CV dataset exploration reports using the Explorer API. For inspiration, check out the

Apps Built Using Ultralytics Explorer

Try our GUI Demo based on Explorer API

Coming Soon

  • [ ] Merge specific labels from datasets. Example - Import all person labels from COCO and car labels from Cityscapes
  • [ ] Remove images that have a higher similarity index than the given threshold
  • [ ] Automatically persist new datasets after merging/removing entries
  • [ ] Advanced Dataset Visualizations


Created 2024-01-07, Updated 2024-06-18
Authors: glenn-jocher (11), 0xSynapse (1), RizwanMunawar (2), AyushExel (2)

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