Detect duplicates with Annoy

We use embeddings to detect duplicates by computing nearest neighbors with the Annoy library. Although the example is based on image embeddings, the basic play is independent of the data type.

Use Chrome to run Spotlight in Colab. Due to Colab restrictions (e.g. no websocket support), the performance is limited. Run the notebook locally for the full Spotlight experience.

inputs

• df['embedding'] contain the embeddings for each data sample

outputs

• df['nn_id'] contains the sample id for the nearest neighbor in the embedding space.
• df['nn_image'] contains the path to the image that is the nearest neighbor.
• df['nn_distance'] contains distance to the nearest neighbor.
• df['nn_flag'] contains a flag that indicates if the sample is a near-duplicate according to the given threshold.

parameters

• threshold denotes the distance threshold when a data sample is considered a near-duplicate.
• tree_size is an internal parameter for the Annoy library that calibrates the speed-efficiency tradeoff. More trees gives higher precision when querying.

Imports and play as copy-n-paste functions

# Install dependencies

#@title Install required packages with PIP

!pip install renumics-spotlight datasets annoy

# Play as copy-n-paste functions

#@title Play as copy-n-paste functions

import datasets
from renumics import spotlight
from annoy import AnnoyIndex
import pandas as pd
import requests


def nearest_neighbor_annoy(df, embedding_name='embedding', threshold=0.3, tree_size=100):

    embs = df[embedding_name]

    t = AnnoyIndex(len(embs[0]), 'angular')

    for idx, x in enumerate(embs):
          t.add_item(idx, x)

    t.build(tree_size)

    images = df['image']

    df_nn = pd.DataFrame()

    nn_id = [t.get_nns_by_item(i,2)[1] for i in range(len(embs))]
    df_nn['nn_id'] = nn_id
    df_nn['nn_image'] = [images[i] for i in nn_id]
    df_nn['nn_distance'] = [t.get_distance(i, nn_id[i]) for i in range(len(embs))]
    df_nn['nn_flag'] = (df_nn.nn_distance < threshold)


    return df_nn

Step-by-step example on CIFAR-100

Load CIFAR-100 from Huggingface hub and convert it to Pandas dataframe

dataset = datasets.load_dataset("renumics/cifar100-enriched", split="train")
df = dataset.to_pandas()

Compute nearest neighbors including distances

df_nn = nearest_neighbor_annoy(df)
df = pd.concat([df, df_nn], axis=1)

Inspect and remove duplicates with Spotlight

df_show = df.drop(columns=['embedding', 'probabilities'])
layout_url = "https://raw.githubusercontent.com/Renumics/spotlight/playbook_initial_draft/playbook/rookie/duplicates_annoy.json"
response = requests.get(layout_url)
layout = spotlight.layout.nodes.Layout(**json.loads(response.text))
spotlight.show(df_show, dtype={"image": spotlight.Image, "embedding_reduced": spotlight.Embedding}, layout=layout)