Upload sedimentary-features-9eosf from RF100

README.dataset.txt

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+# sedimentary features > release-640
+https://universe.roboflow.com/roboflow-100/sedimentary-features-9eosf
+
+Provided by a Roboflow user
+License: CC BY 4.0
+
+This dataset was originally created by [Ammar Abdlmutalib](https://universe.roboflow.com/sedimentary-structures/). To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/sedimentary-structures/sedimentary-features-rmadz.
+
+This dataset is part of [RF100](https://rf100.org), an [Intel-sponsored](https://www.intel.com/) initiative to create a new object detection benchmark for model generalizability.
+
+Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - object-detection
+tags:
+  - roboflow
+  - roboflow-100
+  - rf100
+  - yolo
+  - libreyolo
+  - real-world
+  - computer-vision
+  - bounding-box
+pretty_name: "Sedimentary Features 9Eosf"
+size_categories:
+  - 1K<n<10K
+dataset_info:
+  features:
+    - name: image
+      dtype: image
+    - name: label
+      dtype: string
+  splits:
+    - name: train
+      num_examples: 156
+    - name: validation
+      num_examples: 45
+    - name: test
+      num_examples: 21
+---
+
+# 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](https://github.com/roboflow/roboflow-100-benchmark)
+- **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](https://universe.roboflow.com/roboflow-100/sedimentary-features-9eosf)
+- Part of the [Roboflow 100 Benchmark](https://www.rf100.org/)
+- Sponsored by Intel

README.roboflow.txt

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+
+sedimentary features - v4 release-640
+==============================
+
+This dataset was exported via roboflow.com on April 3, 2023 at 9:46 PM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand and search unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+For state of the art Computer Vision training notebooks you can use with this dataset,
+visit https://github.com/roboflow/notebooks
+
+To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
+
+The dataset includes 222 images.
+Sediment are annotated in YOLOv8 format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+* Resize to 640x640 (Stretch)
+
+No image augmentation techniques were applied.
+
+

data.yaml

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+names:
+- Cross bedding
+- Low angle
+- Massive
+- Parallel lamination
+- mud drape
+nc: 5
+path: .
+roboflow:
+  license: CC BY 4.0
+  project: sedimentary-features-9eosf
+  url: https://universe.roboflow.com/roboflow-100/sedimentary-features-9eosf/dataset/4
+  version: 4
+  workspace: roboflow-100
+test: test/images
+train: train/images
+val: valid/images

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