Logistics Computer Vision Project
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Logistics Pre-trained Object Detection Model
Pre-trained models are trained on large datasets until they achieve good generalization, meaning they can recognize patterns effectively. "pre-trained" indicates that the model has already undergone training on a substantial dataset, often a generic one, and is ready for fine-tuning on a specific task with a smaller dataset. The Logistics Object Detection Base Model is a pre-trained model hosted on Roboflow Universe, created to be a strong starting point for custom training on logistics-specific object detection tasks. This model is built on a dataset of 99,238 images across 20 logistics-focused classes, collected from various projects on Roboflow Universe. Part of this dataset was auto-labeled using the Autodistill DETIC tool from Roboflow, helping to achieve a mean Average Precision (mAP) of 76%.
Classes:
- Barcode, QR Code
- Car, Truck, Van
- Cardboard Box, Wood Pallet, Freight Container
- Fire, Smoke
- Forklift
- Gloves, Helmet, Safety Vest
- Ladder
- License Plate
- Person
- Road Sign, Traffic Cone, Traffic Light
Current Status: The model has achieved a mAP of 76%, marking its readiness as a checkpoint for further custom training. It aims to shorten the development cycle, facilitating better model performance in specific logistics scenarios.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
logistics-sz9jr_dataset,
title = { Logistics Dataset },
type = { Open Source Dataset },
author = { Large Benchmark Datasets },
howpublished = { \url{ https://universe.roboflow.com/large-benchmark-datasets/logistics-sz9jr } },
url = { https://universe.roboflow.com/large-benchmark-datasets/logistics-sz9jr },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { aug },
note = { visited on 2024-11-21 },
}