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Datasets:
harpreetsahota
/
visual_ai_at_neurips2025_jina

Tasks:
Image Classification
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fiftyone
image
image-classification
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[ "Computer Vision", "Graphics", "Image and Video Processing", "Multimedia", "Robotics" ]
(none)
fiftyone.utils.eval.classification.ClassificationResults
null
["cs.CV","cs.CV","cs.CV","cs.CV","cs.RO","cs.CV","cs.CV","cs.RO","cs.CV","cs.CV","cs.CV","cs.CV","cs(...TRUNCATED)
["eess.IV","eess.IV","eess.IV","eess.IV","cs.RO","eess.IV","eess.IV","cs.RO","eess.IV","eess.IV","ee(...TRUNCATED)
[0.3071403503417969,0.3267194628715515,0.2812448740005493,0.33016470074653625,0.31849855184555054,0.(...TRUNCATED)
null
["69092ad56ff4d31845e5a0bf","69092ad56ff4d31845e5a0c0","69092ad56ff4d31845e5a0c1","69092ad56ff4d3184(...TRUNCATED)
["6913a42acedd6e2a60dc9367","6913a42bcedd6e2a60dc9368","6913a42ccedd6e2a60dc9369","6913a42dcedd6e2a6(...TRUNCATED)
[ "cs.CV", "cs.GR", "cs.MM", "cs.RO", "eess.IV" ]
(none)

Dataset Card for Voxel51/visual_ai_at_neurips2025

This is a FiftyOne dataset with 1134 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage 

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("harpreetsahota/visual_ai_at_neurips2025_jina")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details

Dataset Description

  • Curated by: [More Information Needed]
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  • Shared by [optional]: [More Information Needed]
  • Language(s) (NLP): en
  • License: [More Information Needed]

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  • Repository: [More Information Needed]
  • Paper [optional]: [More Information Needed]
  • Demo [optional]: [More Information Needed]

Uses

Direct Use

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Out-of-Scope Use

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Dataset Structure

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Dataset Creation

Curation Rationale

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Source Data

Data Collection and Processing

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Who are the source data producers?

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Annotations [optional]

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Bias, Risks, and Limitations

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Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation [optional]

BibTeX:

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