Component_Recognition_v2 Object Detection Dataset and Pre-Trained Model by EEIA2022

Images

339 images

Explore Dataset

Here are a few use cases for this project:

Automated Inventory Management: Utilizing Component_Recognition_v2 to automatically identify and categorize electronic components in a warehouse, enabling efficient inventory tracking, supply chain optimization, and reducing human-errors in stocktaking.
PCB (Printed Circuit Board) Inspection and Quality Control: Analyzing images of assembled PCBs, detecting incorrect or misaligned components, ensuring the boards are functioning correctly, reducing failure rates, and minimizing production costs.
Electronics Recycling and Disposal: Applying the model to sort images of discarded electronic waste, quickly identifying the components, and facilitating the proper recycling, disposal, or repurposing of materials in an environmentally friendly manner.
Educational Resources and Tutorials: Enhancing electronic DIY projects, repair guides, and online courses by automatically labeling component images with their classifications, making it easier for students and hobbyists to learn and understand electronics projects.
Maintenance and Troubleshooting Support: Assisting technicians in diagnosing malfunctions and identifying parts requiring replacement in electronic devices, improving maintenance efficiency and reducing equipment downtime.

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{
                            component_recognition_v2_dataset,
                            title = { Component_Recognition_v2 Dataset },
                            type = { Open Source Dataset },
                            author = { EEIA2022 },
                            howpublished = { \url{ https://universe.roboflow.com/eeia2022-dte9r/component_recognition_v2 } },
                            url = { https://universe.roboflow.com/eeia2022-dte9r/component_recognition_v2 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { dec },
                            note = { visited on 2024-07-27 },
                            }