TrevTron/rtl-ml-dataset

RTL-ML Dataset

Dataset Summary

This dataset contains 240 validated RF signal samples captured using an RTL-SDR Blog V4 dongle. It's designed for training machine learning models to classify common RF signals.

Total Size: 1.9 GB
Samples: 240 (30 samples × 8 classes)
Format: NumPy arrays (.npy files)
Sample Rate: 1.024 MSPS
Sample Duration: 1 second per capture

Signal Classes

Class	Frequency	Count	Description
ADS_B	1090 MHz	30	Aircraft transponder signals
APRS	144.39 MHz	30	Amateur radio position reporting
FM_broadcast	88-108 MHz	30	Commercial FM radio stations
ISM_sensors	433.92 MHz	30	Wireless sensors & remote controls
NOAA_APT	137.5 MHz	30	Weather satellite imagery
NOAA_weather	162.4 MHz	30	Weather radio broadcasts
noise	Various	30	Background RF noise baseline
pager	931.9375 MHz	30	POCSAG pager transmissions

Validation Metrics

• ISM Sensors: 20.6x burst ratio (strong on/off keying)
• NOAA Weather: 14.4 dB SNR (clear signal)
• Pager/APRS: 12.7 dB SNR (good quality)
• Model Accuracy: 87.5% on test set

Usage

from huggingface_hub import snapshot_download
import numpy as np

# Download entire dataset
dataset_path = snapshot_download(
    repo_id="TrevTron/rtl-ml-dataset",
    repo_type="dataset"
)

# Load a sample
sample = np.load(f"{dataset_path}/datasets_validated/ADS_B_0.npy")
print(f"Signal shape: {sample.shape}")  # (1048576,) complex64

Dataset Structure

rtl-ml-dataset/
└── datasets_validated/
    ├── ADS_B_0.npy ... ADS_B_29.npy       (30 files)
    ├── APRS_0.npy ... APRS_29.npy         (30 files)
    ├── FM_broadcast_0.npy ... _29.npy     (30 files)
    ├── ISM_sensors_0.npy ... _29.npy      (30 files)
    ├── NOAA_APT_0.npy ... NOAA_APT_29.npy (30 files)
    ├── NOAA_weather_0.npy ... _29.npy     (30 files)
    ├── noise_0.npy ... noise_29.npy       (30 files)
    └── pager_0.npy ... pager_29.npy       (30 files)

Each .npy file contains:

• Shape: (1048576,) - 1 second @ 1.024 MSPS
• Dtype: complex64 (I/Q samples)
• Size: ~8.4 MB per file

Hardware

• SDR: RTL-SDR Blog V4 ($39.95)
• Computer: Indiedroid Nova 16GB ($179.95)
• Antenna: Telescopic dipole (included)

Model Performance

When trained with Random Forest (100 trees):

• Overall Accuracy: 87.5%
• Perfect Classes: ADS-B, FM, ISM, NOAA APT, Weather, Pager (100%)
• Challenging: APRS ↔ Noise confusion (sparse packets)

Citation

@misc{rtl-ml-dataset,
  author = {TrevTron},
  title = {RTL-ML Dataset: Validated RF Signal Captures},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/TrevTron/rtl-ml-dataset}}
}

License

MIT License - Free for commercial and non-commercial use.

Related

• Code: github.com/TrevTron/rtl-ml
• Blog: unland.dev (coming soon)
• Hardware Guide: Indiedroid Nova Setup

Contributions

Captured in Temecula, CA (Southern California) using:

• Clear line of sight to multiple signal sources
• Validated with spectral analysis and manual inspection
• All samples meet minimum SNR requirements (>10 dB for modulated signals)

For questions or improvements, see the GitHub repository.