Usability
8.8/10Consistent formatting and concise metadata make exploration accessible.
Loading page...
Loading page...
Loading page...
Loading page...
Loading page...
Computer Vision dataset prepared for reproducible experimentation and practical model evaluation.
913
0
N/A
5.6 GB
Road-scene image dataset with annotated signs and lane markers captured from Algerian highways and urban roads.
North Africa Road Sign Detection aggregates domain-specific records from University Transport Lab with a structure designed for quick downstream analysis and training.
Files include core records, lightweight metadata, and machine-learning-ready fields. The layout is intended for straightforward ingestion with common Python or JavaScript data tooling.
Published by University Transport Lab and community contributors supporting open Algerian AI resources.
1
| File | Size | Description |
|---|---|---|
| main.zip | 5.6 GB | Primary data split for training and analysis. |
Browse files, inspect rows, and sort columns before downloading.
Files
Select a file to preview.
| Column | Type | Completeness | Description |
|---|---|---|---|
| record_id | string | 100% | Stable record identifier. |
| source_name | string | 100% | Origin reference for University Transport Lab collection workflow. |
| tag_1 | string | 100% | Tag marker for Autonomous Driving use cases and filtering. |
| tag_2 | string | 100% | Tag marker for Detection use cases and filtering. |
| updated_at | datetime | 100% | Last update timestamp for each row. |
Consistent formatting and concise metadata make exploration accessible.
Core fields are strong; deeper long-tail metadata remains incremental.
Source pedigree is clear and suitable for experimentation.
Regular updates are present but should continue scaling with demand.
Community Member
Practitioner
12 Dec 2025
★★★★☆
Solid dataset for practical model development and benchmarking.