Test Object Detection Dataset and Pre-Trained Model by enpo

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Explore Dataset

Here are a few use cases for this project:

Traffic Analysis: This model could be applied in real-time traffic surveillance systems to count and categorize different types of vehicles on the road. This data can be helpful for optimizing traffic flow, predicting congestion, and improving infrastructure.
Smart Retail Solutions: The model can help identify and track items within retail stores, such as shopping carts, or shoppers carrying suitcases, mobile phones, etc. This can contribute to theft prevention, improve customer service, and optimize store layout.
Public Safety: The model can be utilized in public areas to detect and classify objects, supporting law enforcement in identifying suspicious activities, such as abandoned suitcases or unusual behavior.
Urban Planning: The model can analyze the variety and number of vehicles, bicycles, and pedestrians in various parts of a city. With this data, city planners can make more informed decisions about transportation infrastructure development, pedestrian path planning, and parking facilities.
Facility Management: In office settings or large public buildings, the model can help identify how spaces and furniture are used, like identifying empty chairs, tracking the usage of toilets, or keeping track of potted plants' locations. This can provide important insights for optimizing space use.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

YOLOv8

This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.

Cite This Project

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

@misc{
                            test-mxlfe_dataset,
                            title = { Test Dataset },
                            type = { Open Source Dataset },
                            author = { enpo },
                            howpublished = { \url{ https://universe.roboflow.com/enpo-5csjs/test-mxlfe } },
                            url = { https://universe.roboflow.com/enpo-5csjs/test-mxlfe },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-04-26 },
                            }