Logistics Dataset

## 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](https://github.com/autodistill/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.

物流领域预训练目标检测模型

预训练模型经过大规模数据集的训练，直至达到良好的泛化能力，即能够有效地识别模式。‘预训练’一词表明该模型已在大量数据集上完成训练，通常为通用数据集，并已准备好在特定任务上使用较小数据集进行微调。物流目标检测基础模型是托管在Roboflow宇宙上的预训练模型，旨在作为针对物流特定目标检测任务进行定制训练的强大起点。该模型基于Roboflow宇宙中来自各种项目的99,238张图像数据集构建，包含**20个专注于物流的类别**。该数据集的部分图像通过Roboflow提供的[Autodistill DETIC](https://github.com/autodistill/autodistill-detic)工具自动标注，有助于实现平均精度均值（mAP）为76%的平均水平。

**类别：**
* 条形码，二维码
* 汽车，卡车，面包车
* 纸箱，木托盘，货运集装箱
* 火，烟
* 叉车
* 手套，头盔，安全背心
* 梯子
* 车牌
* 人员
* 交通标志，交通锥，交通灯

**当前状态：**
该模型已实现mAP为76%，标志着其作为进一步定制训练的检查点的准备就绪。其目标在于缩短开发周期，促进在特定物流场景中模型性能的优化。