metadata

annotations_creators: []
language: en
size_categories:
  - n<1K
task_categories:
  - image-classification
task_ids: []
pretty_name: validation_photos
tags:
  - fiftyone
  - image
  - image-classification
dataset_summary: >

  These photos are used in the FiftyOne getting started webinar. The images have
  a prediction label where were generated by  self-supervised classification
  through a OpenClip Model. 
  https://github.com/thesteve0/fiftyone-getting-started/blob/main/5_generating_labels.py
  They were then manually cleaned to produce the ground truth label. 
  https://github.com/thesteve0/fiftyone-getting-started/blob/main/6_clean_labels.md

  They are public domain photos taken from Flickr. They will be used to evaluate
  classification models. They are purely for teaching purposes and are not
  recommended for any other use.  No other guarantees provided.  


  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 143
  samples.


  ## Installation


  If you haven't already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  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("TheSteve0/getting-started-labeled-validation")


  # Launch the App

  session = fo.launch_app(dataset)

  ```

Dataset Card for validation_photos

This is a FiftyOne dataset with 143 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("TheSteve0/getting-started-labeled-validation")

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

Dataset Details

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