---
title: cables-nl42k
canonical_url: "https://www.modelscope.cn/datasets/LibreYOLO/cables-nl42k"
md_url: "https://www.modelscope.cn/datasets/LibreYOLO/cables-nl42k.md"
repository: LibreYOLO/cables-nl42k
last_updated: 2026-08-28
license: cc-by-4.0
downloads: 27047
stars: 0
---

# cables-nl42k

> cables-nl42k - LibreYOLO 在 ModelScope 开源的数据集。This dataset is part of the Roboflow 100 benchmark, a diverse collection of 100 object detection datasets spanning 7 imagery domains.

LibreYOLO/cables-nl42k 是 ModelScope 魔搭社区上的数据集，采用 cc-by-4.0 许可。

- **Repository**: LibreYOLO/cables-nl42k
- **License**: cc-by-4.0
- **Downloads**: 27047
- **Stars**: 0
- **Last updated**: 2026-08-28

Source: https://www.modelscope.cn/datasets/LibreYOLO/cables-nl42k

---

# Cables Nl42K

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:** 2026-01-20

## Dataset Statistics

| Split | Images |
|-------|--------|
| Train | 4,816 |
| Validation | 1,220 |
| Test | 794 |
| **Total** | **6,830** |

## Classes (11)

  - Antenne
  - BBS
  - BFU
  - Batterie
  - DDF
  - PCF
  - PCU AC
  - PCU DC
  - PDU
  - PSU
  - RBS

## 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/cables-nl42k",
    repo_type="dataset",
    local_dir="./cables-nl42k"
)
```

## Directory Structure

```
cables-nl42k/
├── 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/cables-nl42k)
- Part of the [Roboflow 100 Benchmark](https://www.rf100.org/)
- Sponsored by Intel