feat: add datasets

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+*.png filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+pretty_name: UVH-26 (Urban Vision Hackathon Dataset)
+license: cc-by-4.0
+tags:
+  - computer-vision
+  - object-detection
+  - traffic
+  - vehicles
+  - india
+  - cctv
+task_categories:
+  - object-detection
+task_ids:
+  - vehicle-detection
+language:
+  - und
+annotations_creators:
+  - crowd-sourced
+  - expert-generated
+source_datasets: []
+size_categories:
+  - 10K<n<100K
+dataset_info:
+  features:
+    - name: image
+      dtype: image
+    - name: objects
+      sequence:
+        - name: bbox
+          dtype:
+            type: sequence
+            length: 4
+            feature:
+              dtype: float32
+        - name: category
+          dtype: class_label
+          names:
+            - Hatchback
+            - Sedan
+            - SUV
+            - MUV
+            - Bus
+            - Truck
+            - auto
+            - bike
+            - LCV
+            - Mini-bus
+            - tempo-traveller
+            - bicycle
+            - Van
+            - Other
+  splits:
+    - name: all
+      num_examples: 26646
+      dataset_name: UVH-26
+  download_size: null
+  dataset_size: null
+pretty_metadata:
+  summary: >
+    UVH-26 is a large-scale India-specific traffic-camera dataset released by AIM@IISc.
+    It contains 26,646 1080p images from ~2,800 Bengaluru Safe-City CCTV cameras over ~4 weeks,
+    annotated via a nationwide crowdsourced hackathon (565 students) with ~1.8M bounding boxes
+    across 14 vehicle classes. Two consensus versions are provided: Majority Voting and STAPLE.
+homepage: https://huggingface.co/datasets/iisc-aim/UVH-26/tree/v1.0
+---
+
+# Dataset Card for UVH-26 (Urban Vision Hackathon Dataset)
+
+## Dataset Summary
+**UVH-26** is a large-scale, India-specific traffic-camera image dataset released by **AIM @ IISc** for research in intelligent transportation systems and vehicle detection.  
+It contains **26,646** high-resolution (1080p) frames sampled from ≈ 2,800 Bengaluru *Safe City* CCTV cameras over a 4-week period.  
+Images were annotated through a nationwide crowdsourced hackathon involving **565 college students**, producing **≈ 1.8 million bounding boxes** across **14 fine-grained vehicle classes** representative of Indian traffic conditions.
+
+To capture different levels of annotation consensus, UVH-26 includes **two separate annotation sets**:
+1. **`UVH-26-MV`** — final labels computed via *majority voting* across multiple annotators per image.  
+2. **`UVH-26-ST`** — labels generated using the *STAPLE* algorithm (an Expectation–Maximization–based probabilistic consensus method) for higher reliability.
+
+These versions share identical image data but differ in bounding box consensus logic.
+
+## Dataset Structure
+
+The datasets released follow the folder structure described below.
+
+### **1. UVH-26-Train/**
+Contains **80% of the UVH-26 dataset** used for training.
+* **`images/`** – Training images organized into subfolders (`000/`, `001/`, …) for convenience.
+  * `images/000/*` – Actual training images (`1.png`, `2.png`, …). Each image filename is unique across the entire dataset.
+  * `images/001/*`, etc. – Additional subfolders following the same structure.
+* **`UVH-26-MV-Train.json`** – Majority Voting consensus annotations for training images in **COCO JSON format**.
+* **`UVH-26-ST-Train.json`** – STAPLE consensus annotations for training images in **COCO JSON format**.
+
+### **2. UVH-26-Val/**
+Contains **20% of the UVH-26 dataset** used for validation.
+* **`images/`** – Validation images organized into subfolders (`000/`, `001/`, …).
+  * `images/000/*` – Actual validation images. All filenames are globally unique across both training and validation sets.
+  * `images/001/*`, etc. – Additional subfolders following the same structure.
+* **`UVH-26-MV-Val.json`** – Majority Voting consensus annotations for validation images in **COCO JSON format**.
+* **`UVH-26-ST-Val.json`** – STAPLE consensus annotations for validation images in **COCO JSON format**.
+
+## Annotation JSON Schema
+Each annotation file follows the standard COCO structure:
+- **`images`** — list of image metadata  
+  `id`, `file_name`, `width`, `height`
+- **`annotations`** — object instances  
+  `id`, `image_id`, `category_id`, `bbox [x, y, width, height]`, `area`  
+- **`categories`** — class taxonomy (IDs and names below)
+
+### Annotation Pipeline
+- **Source:** frames captured between 06:00 – 18:00 IST during February 2025  
+- **Pre-annotation:** generated using a fine-tuned **RT-DETR v2-X** model trained on ≈ 3 k expert-labeled images  
+- **Crowdsourcing:** > 550 student volunteers corrected or validated predictions through a gamified web interface with leaderboards  
+- **Consensus:** both *majority voting* and *STAPLE* algorithms applied to derive final annotations
+
+## Vehicle Classes
+| ID | Class Name        | Description                                                                                                                                            |
+| -- | ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| 1  | Hatchback         | Small passenger cars without a protruding rear boot (“dickey”).                                                                                        |
+| 2  | Sedan             | Passenger cars with a low-slung design and a separate protruding rear boot (“dickey”).                                                                 |
+| 3  | SUV               | Car-like vehicles with high ground clearance, a sturdy body, and no protruding boot.                                                                   |
+| 4  | MUV               | Large vehicles with three seating rows, combining passenger and cargo functionality.                                                                   |
+| 5  | Bus               | Large passenger vehicles used for public or private transport, including office shuttles and intercity buses.                                          |
+| 6  | Truck             | Heavy goods carriers with a front cabin and a rear cargo compartment.                                                                                  |
+| 7  | Three-wheeler     | Compact vehicles with one front wheel and two rear wheels, featuring a covered passenger cabin.                                                        |
+| 8  | Two-wheeler       | Motorbikes and scooters for single or double riders. Bounding boxes include both vehicle and rider.                                                    |
+| 9  | LCV               | Lightweight goods carriers used for short- to medium-distance transport.                                                                               |
+| 10 | Mini-bus          | Shorter, compact buses with fewer seats; larger than a Tempo Traveller, often featuring a flat front.                                                  |
+| 11 | Tempo-traveller   | Medium-sized passenger vans with tall roofs and side windows; larger than vans but smaller than minibuses, with a protruding front.                    |
+| 12 | Bicycle           | Non-motorized, manually pedalled vehicles including geared, non-geared, women’s, and children’s cycles. Bounding boxes include both vehicle and rider. |
+| 13 | Van               | Medium-sized vehicles for transporting goods or people, typically with a flat front and sliding side doors; smaller than Tempo Travellers.             |
+| 14 | Other             | Vehicles not covered in other classes, including agricultural, specialized, or unconventional designs.                                                  |
+
+## Collection and Processing
+- **Source:** ≈ 2,800 *Safe City* surveillance cameras operated by Bengaluru Police  
+- **Coverage:** both junction and mid-block perspectives across multiple city zones  
+- **Selection:** images with high vehicle density, occlusion, and diverse viewpoints prioritized  
+
+## Intended Uses
+- Building accurate, lightweight, edge-deployed perception systems for **Intelligent Transportation Systems (ITS)**  
+- Training and benchmarking vehicle detection models
+
+## License
+- **Dataset:** [CC BY 4.0 International](https://creativecommons.org/licenses/by/4.0/)  
+- **Pre-trained Models:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
+## Acknowledgements
+We thank the **Bengaluru Traffic Police (BTP)** and the **Bengaluru Police** for providing access to the *Safe City* camera data from which the image datasets used for this release were derived.  
+We thank **Capital One** for sponsoring the prizes for the **Urban Vision Hackathon** competition.  
+We thank **IISc’s AI and Robotics Technology Park (ARTPARK)** and the **Centre for Infrastructure, Sustainable Transportation and Urban Planning (CiSTUP)** for funding the annotation and model-training efforts, and the **Kotak IISc AI-ML Centre (KIAC)** for providing the GPU resources required to train the models.  
+We acknowledge the outreach support provided by the **ACM India Council** and the **IEEE India Council** to encourage chapter volunteers to participate in the hackathon.  
+Lastly, we thank the **AI Centers of Excellence (AI COE)** initiative of the **Ministry of Education**, their **Apex Committee members**, and the **AIRAWAT Research Foundation**, whose support helped catalyze these efforts.  
+
+Created by the **AI for Integrated Mobility (AIM)** group at the **Indian Institute of Science (IISc)**, Bengaluru.

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