Datasetpublic

LibreYOLO/sedimentary-features-9eosf

Sedimentary Features 9Eosf This dataset is part of the Roboflow 100 benchmark, a diverse collection of 100 object detection datasets spanning 7 imagery domains. Dataset Statistics Split Images Train 156 Validation 45 Test 21 Total 222 Classes (5) Cross bedding Low angle Massive Parallel lamination mud drape Usage With LibreYOLO from libreyolo import LIBREYOLO # Load a model model =… See the full description on the dataset page: https://huggingface.co/datasets/LibreYOLO/sedimentary-features-9eosf.

sourceHugging Facecc-by-4.0updated 8mo agoView on Hugging Face

0likes56downloads

Dataset Card

Sedimentary Features 9Eosf

This dataset is part of the Roboflow 100 benchmark, a diverse collection of 100 object detection datasets spanning 7 imagery domains.

Dataset Description

—Source: Roboflow 100
—Category: Real World
—License: CC-BY-4.0
—Format: YOLO (LibreYOLO compatible)
—Mirrored on: 2026-01-21

Dataset Statistics

Split	Images
Train	156
Validation	45
Test	21
Total	222

Classes (5)

—Cross bedding
—Low angle
—Massive
—Parallel lamination
—mud drape

Usage

With LibreYOLO

python

from libreyolo import LIBREYOLO

# Load a model
model = LIBREYOLO(model_path="libreyoloXnano.pt")

# Train on this dataset
model.train(data='path/to/data.yaml', epochs=100)

Download from HuggingFace

python

from huggingface_hub import snapshot_download

# Download the dataset
snapshot_download(
    repo_id="Libre-YOLO/sedimentary-features-9eosf",
    repo_type="dataset",
    local_dir="./sedimentary-features-9eosf"
)

Directory Structure

sedimentary-features-9eosf/
├── data.yaml           # Dataset configuration
├── README.md           # This file
├── train/
│   ├── images/         # Training images
│   └── labels/         # Training labels (YOLO format)
├── valid/
│   ├── images/         # Validation images
│   └── labels/         # Validation labels
└── test/
    ├── images/         # Test images (if available)
    └── labels/         # Test labels

Label Format

Labels are in YOLO format (one .txt file per image):

<class_id> <x_center> <y_center> <width> <height>

All coordinates are normalized to [0, 1].

Citation

If you use this dataset, please cite the Roboflow 100 benchmark:

bibtex

@misc{rf100_2022,
    Author = {Floriana Ciaglia and Francesco Saverio Zuppichini and Paul Guerrie and Mark McQuade and Jacob Solawetz},
    Title = {Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark},
    Year = {2022},
    Eprint = {arXiv:2211.13523},
}

License

This dataset is released under the CC-BY-4.0 license. Please check the original source for any additional terms.

Acknowledgments

—Original dataset from Roboflow Universe
—Part of the Roboflow 100 Benchmark
—Sponsored by Intel