getting-started
getting-started-labeled-validation
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)… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/getting-started-labeled-validation.getting-started-validation-clip-pred
Dataset Card for labeled_validation_predicted_clip
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-validation-clip-pred")
# Launch the App
session =… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/getting-started-validation-clip-pred.getting-started-labeled-photos
Dataset Card for predicted_labels
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 300 public domain photos… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/getting-started-labeled-photos.nlp-getting-startedbluemap.bluecolored.de.wiki.getting.started.Commands.htmlembeddings_getting_started_test