Datasets:
File size: 2,896 Bytes
46e6eba |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 |
---
license: cc-by-4.0
task_categories:
- object-detection
tags:
- roboflow
- roboflow-100
- rf100
- yolo
- libreyolo
- aerial
- computer-vision
- bounding-box
pretty_name: "Cloud Types"
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: string
splits:
- name: train
num_examples: 3528
- name: validation
num_examples: 1008
- name: test
num_examples: 504
---
# Cloud Types
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:** Aerial
- **License:** CC-BY-4.0
- **Format:** YOLO (LibreYOLO compatible)
- **Mirrored on:** 2026-01-21
## Dataset Statistics
| Split | Images |
|-------|--------|
| Train | 3,528 |
| Validation | 1,008 |
| Test | 504 |
| **Total** | **5,040** |
## Classes (4)
- Fish
- Flower
- Gravel
- Sugar
## 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/cloud-types",
repo_type="dataset",
local_dir="./cloud-types"
)
```
## Directory Structure
```
cloud-types/
├── 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/cloud-types)
- Part of the [Roboflow 100 Benchmark](https://www.rf100.org/)
- Sponsored by Intel
|