Traffic_Object_detection Computer Vision Project

Internship

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Classes (23)
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Random Gaussian blur of between 0 and 2.5 pixels
* Random rotation of between -15 and +15 degrees
* Resize to 640x640 (Stretch)
* annotate, and create datasets
* collaborate with your team on computer vision projects
* collect & organize images
* export, train, and deploy computer vision models
* understand and search unstructured image data
* use active learning to improve your dataset over time
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Cars are annotated in YOLOv9 format.
For state of the art Computer Vision training notebooks you can use with this dataset,
Roboflow is an end-to-end computer vision platform that helps you
The dataset includes 16020 images.
The following augmentation was applied to create 3 versions of each source image:
The following pre-processing was applied to each image:
This dataset was exported via roboflow.com on June 9, 2024 at 3:48 PM GMT
To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
vehicle detection in different environmental conditions - v3 2024-06-09 9:06pm
visit https://github.com/roboflow/notebooks

A description for this project has not been published yet.

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Cite This Project

LICENSE
CC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            traffic_object_detection-q76da_dataset,
                            title = { Traffic_Object_detection Dataset },
                            type = { Open Source Dataset },
                            author = { Internship },
                            howpublished = { \url{ https://universe.roboflow.com/internship-9qgxs/traffic_object_detection-q76da } },
                            url = { https://universe.roboflow.com/internship-9qgxs/traffic_object_detection-q76da },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { jul },
                            note = { visited on 2024-11-16 },
                            }