Autodistill Experiment

## Overview

Autodistill Experiment is a dataset for object detection tasks - it contains Household Objects annotations for 1,601 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Autodistill Firenet Tl

## Overview

Autodistill Firenet Tl is a dataset for object detection tasks - it contains Fire Safety Equipment annotations for 1,314 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

AutoDistill FireNet AD

## Overview

AutoDistill FireNet AD is a dataset for object detection tasks - it contains Fire Safety Equipment JZCW annotations for 1,314 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

AutoDistill FireNet AD 2

## Overview

AutoDistill FireNet AD 2 is a dataset for object detection tasks - it contains Fire Safety Equipment T1ec annotations for 1,314 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

This project contains the milk bottle dataset used in examples showing how to use Autodistill.

With this dataset and Autodistill, you can train a model that identifies milk bottles and bottle tops without labeling any images.

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Testingtestingtesting

## Overview

Testingtestingtesting is a dataset for object detection tasks - it contains Fire Safety Equipment DNEJ annotations for 1,314 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

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.

Sample Pet Trans (cli Upload)

## Overview

Sample Pet Trans (cli Upload) is a dataset for object detection tasks - it contains Autodistill Annoted annotations for 596 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).