thirdeyelabs/indian-road-dataset

🚗 Indian Road Driving Dataset

The Indian Road Driving Dataset is the largest open dataset of annotated Indian road footage, created by ThirdEye Labs. It addresses the critical gap in autonomous driving datasets for Indian road conditions.

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🌍 Why Indian Roads?

Indian roads present unique challenges absent from existing datasets (BDD100K, nuScenes, Waymo):

• Dense mixed traffic with unpredictable behavior
• Auto-rickshaws, cattle, and informal lane usage
• Extreme lighting conditions
• 63 million vehicles and 1.4 billion people — yet no large-scale annotated dataset existed

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📊 Dataset Statistics

Metric	Value
Total clips	8,441
Annotated frames	646,014
Object detections	6,896,202
Segmentation masks	1,290,463
GPS-tagged frames	✅
Annotation format	BDD100K
Capture device	CP Plus dashcam
Location	Delhi NCR, India
Conditions	Day · Night · Dusk · Rain

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🏷️ Detection Classes (12 classes)

• person — Pedestrians
• rider — Motorcyclists/cyclists with rider
• car — Passenger cars
• truck — Trucks and tempos
• bus — Buses
• motorcycle — Motorcycles (unridden)
• bicycle — Bicycles
• autorickshaw — Auto-rickshaws (tuk-tuks)
• animal — Cattle, dogs, animals on road
• vehicle fallback — Unclassified vehicles
• traffic light — Traffic signals
• traffic sign — Road signs and boards

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📁 Dataset Structure

Data is stored as 646 WebDataset tar shards (data/train-00000-of-00646.tar … data/train-00645-of-00646.tar), each containing ~1,000 frames. Each frame has 3 files inside the shard:

{clip_id}_{frame:04d}.jpg   # keyframe image
{clip_id}_{frame:04d}.png   # segmentation mask
{clip_id}_{frame:04d}.json  # BDD100K annotations (detections + scene attributes)

Standalone annotation files are also provided for convenient bulk access:

annotations/
├── detection.json          # BDD100K format — all 646,014 frames (1.3 GB)
└── scene_attributes.json   # per-clip weather, time of day, scene type
gps/
└── gps_tracks.json         # GPS coordinates per clip

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🚀 Quick Start

Load with 🤗 Datasets

from datasets import load_dataset

ds = load_dataset("thirdeyelabs/indian-road-dataset")
sample = ds["train"][0]
# sample keys: jpg, png, json

Load annotations directly

import json

with open("annotations/detection.json") as f:
    annotations = json.load(f)

# BDD100K format — each entry:
# { "name": "clip_id/frame", "labels": [{ "category": "car", "box2d": {...} }] }

Download with CLI

huggingface-cli download thirdeyelabs/indian-road-dataset --repo-type dataset

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📐 Annotation Format (BDD100K Schema)

{
  "name": "clip_abc123/0042.jpg",
  "timestamp": 1000,
  "attributes": {
    "weather": "clear",
    "scene": "city street",
    "timeofday": "daytime"
  },
  "labels": [
    {
      "id": 1,
      "category": "car",
      "box2d": { "x1": 296.0, "y1": 242.0, "x2": 477.0, "y2": 379.0 },
      "attributes": { "occluded": false, "truncated": false },
      "track_id": 7
    }
  ]
}

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🗺️ GPS Coverage

Every clip includes GPS coordinates, enabling:

• Geographic filtering by route/area
• Speed and trajectory analysis
• Map-based dataset exploration

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🏗️ Production Pipeline

ThirdEye Labs end-to-end ML annotation system:

1. Ingest — raw MP4s from CP Plus dashcams to S3
2. Keyframe extraction — 1 frame/second via FFmpeg
3. GPS parsing — matched from .srt files
4. Object detection — custom YOLO fine-tuned for Indian roads
5. Semantic segmentation — SegFormer for drivable areas
6. Multi-object tracking — ByteTrack across frames
7. Scene classification — weather, lighting, scene type

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📜 License

Creative Commons Attribution 4.0 International (CC BY 4.0)

Free to use, share, and adapt for any purpose (including commercial) with attribution to ThirdEye Labs.

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📚 Citation

@dataset{thirdeyelabs2026indianroad,
  title     = {Indian Road Driving Dataset},
  author    = {ThirdEye Labs},
  year      = {2026},
  url       = {https://huggingface.co/datasets/thirdeyelabs/indian-road-dataset},
  note      = {Released under CC BY 4.0}
}

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🔗 Links

• 🌐 Website: thirdeyelabs.ai
• 🎬 Demo: thirdeyelabs.ai/demo
• 📧 Contact: thirdeyelabs.ai/contact

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