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End of preview.
Edge-AI Traffic Vehicle Detection (UA-DETRAC CCTV)
Part of the Edge-AI Traffic & Vehicle Analytics System repository by thundarstrom.
Dataset Summary
Curated and normalized 23,319 CCTV traffic images from fixed intersection surveillance cameras (UA-DETRAC benchmark). Contains 215,109 annotated bounding boxes in standard YOLO format across 4 vehicle classes: car, bus, truck, and van.
Class Mapping
- Class 0 (
car): 177,403 bboxes (82.5%) - Class 1 (
bus): 3,523 bboxes (1.6%) - Class 2 (
truck): 16,051 bboxes (7.5%) - Class 3 (
van): 18,132 bboxes (8.4%)
Splits
- Train: 18,655 images (80%)
- Validation: 2,332 images (10%)
- Test: 2,332 images (10%)
- Resolution: 640x640 normalized (native 960x540)
How to Access and Download
Using Automated Project Downloader (Extracts automatically)
# Clone / pull and auto-extract dataset
python scripts/download_hf_datasets.py --dataset vehicle --org thundarstrom
Using huggingface_hub Python SDK
from huggingface_hub import snapshot_download
# Download into local dataset directory
local_path = snapshot_download(
repo_id="thundarstrom/traffic-vehicle-detection",
repo_type="dataset",
local_dir="data/datasets/vehicle_detection"
)
print(f"Dataset downloaded to: {local_path}")
Recommended Training / Evaluation Recipe
python scripts/train_models.py --task vehicle --model yolov8s.pt --epochs 100 --imgsz 640 --batch 32
Citation & Maintainer
- Maintained by:
thundarstrom - Project: Edge-AI Real-Time Traffic Violation Detection & ANPR
- License: CC-BY-4.0
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