---
license: cc-by-nc-sa-4.0
task_categories:
  - image-classification
  - tabular-classification
tags:
  - radar
  - mmwave
  - fault-detection
  - autonomous-driving
  - raw-adc
  - benchmark
  - sensor-fusion
  - mamba
  - ssm
size_categories:
  - n<1K
pretty_name: "Rad-R: Raw-ADC Radar Robustness Dataset"
dataset_info:
  - config_name: default
    features:
      - name: clip_id
        dtype: string
      - name: fault_type
        dtype: string
      - name: severity
        dtype: int64
      - name: device_id
        dtype: string
      - name: session_id
        dtype: string
      - name: scene_id
        dtype: string
      - name: run_id
        dtype: string
      - name: source
        dtype: string
      - name: imu_rms_accel_ms2
        dtype: float64
      - name: mean_yaw_deg
        dtype: float64
      - name: board_temp_c
        dtype: float64
      - name: ambient_temp_c
        dtype: float64
      - name: delta_temp_c
        dtype: float64
      - name: gps_lat
        dtype: float64
      - name: gps_lon
        dtype: float64
      - name: platform_speed_mps
        dtype: float64
    splits:
      - name: train
        num_examples: 14
      - name: validation
        num_examples: 3
      - name: test
        num_examples: 3
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.csv
      - split: validation
        path: data/validation.csv
      - split: test
        path: data/test.csv
---

# Rad-R: A Real-World Raw-ADC Dataset and Benchmark for mmWave Radar Robustness

<p align="center">
  <a href="https://mainakmallick.github.io/radr/"><b>Webpage</b></a> |
  <a href="https://github.com/MainakMallick/radr"><b>Code</b></a> |
  <a href="https://github.com/MainakMallick/radr/blob/main/paper/main.tex"><b>Paper</b></a> |
  <a href="https://pypi.org/project/radr/"><b>PyPI</b></a>
</p>

> **This is a demo release with 20 clips.** The full dataset (~5,000 clips) will be available upon paper acceptance at NeurIPS 2026 Evaluations & Datasets Track.

## Overview

Rad-R is the **first mmWave radar dataset** combining:
- **Raw ADC captures** from a TI MMWCAS-RF-EVM 77 GHz cascaded radar (12 TX × 16 Rx = 192 virtual channels)
- **Controlled hardware fault annotations** across 6 fault types at 3 severity levels
- **Synchronized companion sensors**: IMU (BNO055), temperature (DS18B20), GPS (NEO-M9N), camera
- **4 out-of-distribution evaluation splits**: device, day, scene, severity
- **RadRBench**: standardized benchmarking system with 5 evaluation tasks

## Fault Taxonomy

| Fault Type | S0 (Healthy) | S1 (Mild) | S2 (Moderate) | S3 (Severe) |
|---|---|---|---|---|
| healthy | ✓ baseline | — | — | — |
| vibration | — | <2 m/s² RMS | 2-5 m/s² | >5 m/s² |
| misalignment | — | 2-4° yaw | 5-8° | >9° |
| blockage | — | <30% coverage | 30-70% | >70% |
| rx_degradation | — | <3 dB drop | 3-8 dB | >8 dB |
| thermal_stress | — | ΔT 5-10°C | 10-20°C | >20°C |
| rf_interference | — | Far/off-axis | Partial align | Near boresight |

## Data Format

### Metadata (viewable above)
Each row in the dataset viewer shows per-clip metadata including fault type, severity, sensor readings (IMU, temperature, GPS), device ID, and scene ID.

### HDF5 Files (downloadable)
Each clip in the HDF5 files contains:

| Field | Shape | Type | Description |
|---|---|---|---|
| `iq` | (1, 64, 256, 16) | complex64 | Raw IQ tensor (frames, chirps, samples, rx) |
| `rd` | (224, 224) | float32 | Range-Doppler map (log-normalized) |
| `ra` | (224, 224) | float32 | Range-Azimuth map (beamformed) |
| `microdoppler` | (224, 224) | float32 | Micro-Doppler spectrogram |

Plus per-clip attributes: `fault_type`, `severity`, `device_id`, `scene_id`, `session_id`, `run_id`, `sha256`, and all sensor context values.

### File Structure

```
radr.h5        — Full dataset (per-clip HDF5 groups, gzip compressed)
train.h5       — Pre-split flat arrays for fast training (14 clips)
val.h5         — Validation split (3 clips)
test.h5        — Test split (3 clips)
index.json     — Lightweight metadata sidecar
splits.json    — Split definitions
data/          — CSV metadata for HuggingFace viewer
```

## Quick Start

### Option 1: radr Python package
```bash
pip install radr
```

```python
from radr import RadRDataset, RadRBench, build_model

# Load dataset
ds = RadRDataset("path/to/data", representation="rd")
sample = ds[0]  # {"input": (1, 224, 224), "fault_label": int, "severity_label": int}

# Build and benchmark a model
model = build_model("resnet18")
results = RadRBench.evaluate(model, "path/to/data", representation="rd")
RadRBench.print_summary(results)
```

### Option 2: HuggingFace datasets
```python
from datasets import load_dataset
ds = load_dataset("gtaxcenter/radr", split="train")
```

### Available Models (via `build_model()`)
| Model | Input | Type |
|---|---|---|
| `resnet18` | RD/RA/μD maps | Traditional CNN |
| `vit_small` | RD/RA/μD maps | Vision Transformer |
| `iqcnn` | Raw IQ | 1D CNN |
| `radrnet` | Raw IQ | **Hierarchical Mamba (ours)** |
| `clip` | RD maps (RGB) | CLIP ViT-B/32 fine-tune |
| `openclip` | RD maps (RGB) | OpenCLIP ViT-L/14 LoRA |
| `llava_lite` | RD maps | LLaVA vision encoder |
| `qwen2vl` | RD maps | Qwen2-VL vision encoder |
| `paligemma` | RD maps | PaliGemma SigLIP |
| `resnet18_fused` | RD+RA+μD | Multi-rep gated fusion |

## Radar Configuration

| Parameter | Value |
|---|---|
| Hardware | TI MMWCAS-RF-EVM (AWR1243 4-chip cascade) |
| Frequency | 77 GHz |
| Bandwidth | 2.53 GHz |
| ADC samples | 256 per chirp |
| Loops | 64 (Doppler dimension) |
| TDM-MIMO | 12 TX × 16 Rx = 192 virtual channels |
| Range | 0–15.2 m, resolution 0.059 m |
| Velocity | ±21.3 m/s |

## Sensor Suite

| Sensor | Model | Sample Rate | Measurements |
|---|---|---|---|
| IMU | Bosch BNO055 | 100 Hz | 3-axis accel, gyro, magnetometer, euler angles |
| Temperature | 2× DS18B20 | 1 Hz | Board temp (PCB-DSP gap), ambient |
| GPS | u-blox NEO-M9N | 10 Hz | Lat, lon, altitude, speed |
| Camera | RPi Camera v3 | 10 fps | 1280×720 RGB |

## Citation

```bibtex
@inproceedings{mallick2026radr,
  title={Rad-R: A Real-World Raw-ADC Dataset and Benchmark for mmWave Radar Robustness},
  author={Mallick, Mainak and Yim, Junghwan and Choi, Seung-Kyum},
  booktitle={NeurIPS Evaluations \& Datasets Track},
  year={2026}
}
```

## License

CC BY-NC-SA 4.0

## Links

- **Code & Models**: [github.com/MainakMallick/radr](https://github.com/MainakMallick/radr)
- **Paper**: [NeurIPS 2026 submission](https://github.com/MainakMallick/radr/blob/main/paper/main.tex)
- **Lab**: [GT-AX Center, Georgia Institute of Technology](https://ax.gatech.edu)