Datasets
Standard Dataset
DIRS24.v1
- Citation Author(s):
-
SauravKumar
Department of Electrical Engineering, IIT RoorkeeSumathiParasuramanDepartment of Electrical Engineering, IIT Roorkee - Submitted by:
- Parasuraman Sumathi
- Last updated:
- Fri, 04/12/2024 - 07:20
- DOI:
- 10.21227/q3hd-v088
- Data Format:
- License:
421 Views- Categories:
- Keywords:
Abstract
DIRS24.v1 presents a dataset captured in campus environment. These images are curated suitably for the utilization in developing perception modules. These modules can be very well employed in Advanced Driver Assistance Systems (ADAS). The images of dataset are annotated in diversified formats such as COCO-MMDetection, Pascal-VOC, TensorFlow, YOLOv7-PyTorch, YOLOv8-Oriented Bounding Box, and YOLOv9.
Dataset Overview is as follows:
Total Images:
2374 images in .jpg format
Annotations:
The dataset comprises of 5144 annotations categorically distributed as follows:
1. Pedestrian (1353)
2. Car (1219)
3. Motorcyclist (1159)
4. Cyclist (1150)
5. Dog (162)
6. Electric_Auto (85)
7. NMV (Non-Motorized Vehicle) such as thela, rickshaw, bull cart (16)
Annotation:
All images are annotated manually.
Utility:
The dataset serves as a valuable resource for both training and testing of object detection algorithms which are tailored for Advanced Driver Assistance Systems.
Image Resolution:
The median image resolution stands at 1920 X 1080 pixels, ensuring high-quality data for algorithmic training and evaluation.
Diversity:
DIRS24.v1 images are diversified in appearance, scale, illumination, season, and weather conditions, facilitating robust algorithmic training and testing.
Due to the substantial size of this image dataset (1.16 GB), it has been compressed into a zip file for ease of download. To access the dataset, users are required to download and extract the contents of 'DIRS24.v1.zip' using their preferred zip file extraction tool.
