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πŸŽ‰ We released Spotlight 1.4.0 check it out β†’

Version: Next

Find false labels with Cleanlab

We use the Cleanlab library to compute label error scores. We then manually inspect the data points to correct them.

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.

Open In Colab


Imports and play as copy-n-paste functions​

# Install dependencies
#@title Install required packages with PIP

!pip install renumics-spotlight cleanlab datasets
# Play as copy-n-paste functions
#@title Play as copy-n-paste functions

import datasets
from renumics import spotlight
from cleanlab.filter import find_label_issues
import numpy as np
import pandas as pd

def label_error_score_cleanlab(df, probabilities_name='probabilities', label_name='labels'):

probs = np.stack(df[probabilities_name].to_numpy())
labels = df[label_name].to_numpy()

label_issues = find_label_issues(labels, probs)


return df_out

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 label error scores with Cleanlab​

df_le = label_error_score_cleanlab(df, label_name='fine_label')
df = pd.concat([df, df_le], axis=1)

Inspect label errors and remove them with Spotlight​

df_show = df.drop(columns=['embedding', 'probabilities'])
layout_url = ""
response = requests.get(layout_url)
layout = spotlight.layout.nodes.Layout(**json.loads(response.text)), dtype={"image": spotlight.Image, "embedding_reduced": spotlight.Embedding}, layout=layout)