Group_9_V1 Object Detection Dataset and Pre-Trained Model by EGH450 ITA

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Explore Dataset

Here are a few use cases for this project:

Outdoors Equipment Retail: An outdoor equipment store can use this model to analyze customer behavior in the store, e.g., how often people interact with different types of backpacks, their steps towards items, and general patterns of traffic flow.
Surveillance and Security: It can be used to identify suspicious activities in public spaces like airports or train stations, for example, a person leaving a backpack unattended.
Accident Prevention: The model can assist in creating systems that alert authorities when backpacks are left on staircases or in walkways, potentially causing falls or accidents.
Education Environment Monitoring: Schools or colleges can use this model to manage locker spaces efficiently by identifying backpacks that are not stored correctly or left in pathways.
Inventory Management: Retailers or warehouses can use the model to identify where backpacks are placed in storage or inventory spaces, subsequently improving space utilization and product location efficiency.

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.

Cite This Project

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

@misc{
                            group_9_v1_dataset,
                            title = { Group_9_V1 Dataset },
                            type = { Open Source Dataset },
                            author = { EGH450  ITA },
                            howpublished = { \url{ https://universe.roboflow.com/egh450-ita/group_9_v1 } },
                            url = { https://universe.roboflow.com/egh450-ita/group_9_v1 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { oct },
                            note = { visited on 2024-04-19 },
                            }