Yolov4 Tiny

## Overview

Yolov4 Tiny is a dataset for object detection tasks - it contains Apples annotations for 1,214 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).

Pytorch Yolov4 Tiny

## Overview

Pytorch Yolov4 Tiny is a dataset for object detection tasks - it contains Helipad annotations for 1,046 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).

Dataset

This dataset was created by sfr_auliaaa

Released under CC0: Public Domain

Contents

This dataset can be used for Yolo, YoloV2, YoloV3, YoloV3-Tiny, YoloV4, YoloV4-Tiny.

YOLO V4 Tiny Object Detection

## Overview

YOLO V4 Tiny Object Detection is a dataset for object detection tasks - it contains Pothole annotations for 665 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).

Dataset

This dataset was created by Pilot Khadka

Released under MIT

Contents

Yaml file for YOLOv4 tiny

Yolov4 Tiny Vehicle

## Overview

Yolov4 Tiny Vehicle is a dataset for object detection tasks - it contains Vehicle annotations for 643 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).

Yolo V4 Tiny

## Overview

Yolo V4 Tiny is a dataset for object detection tasks - it contains Road Objects annotations for 2,347 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Implementing and deploying advanced technologies are principal in improving manufacturing processes, signifying a transformative stride in the industrial sector. Computer vision plays a crucial innovation role during this technological advancement, demonstrating broad applicability and profound impact across various industrial operations. This pivotal technology is not merely an additive enhancement but a revolutionary approach that redefines quality control, automation, and operational efficiency parameters in manufacturing landscapes. By integrating computer vision, industries are positioned to optimize their current processes significantly and spearhead innovations that could set new standards for future industrial endeavors. However, the integration of computer vision in these contexts necessitates comprehensive training programs for operators, given this advanced system’s complexity and abstract nature. Historically, training modalities have grappled with the complexities of understanding concepts as advanced as computer vision. Despite these challenges, computer vision has recently surged to the forefront across various disciplines, attributed to its versatility and superior performance, often matching or exceeding the capabilities of other established technologies. Nonetheless, there is a noticeable knowledge gap among students, particularly in comprehending the application of Artificial Intelligence (AI) within Computer Vision. This disconnect underscores the need for an educational paradigm transcending traditional theoretical instruction. Cultivating a more practical understanding of the symbiotic relationship between AI and computer vision is essential. To address this, the current work proposes a project-based instructional approach to bridge the educational divide. This methodology will enable students to engage directly with the practical aspects of computer vision applications within AI. By guiding students through a hands-on project, they will learn how to effectively utilize a dataset, train an object detection model, and implement it within a microcomputer infrastructure. This immersive experience is intended to bolster theoretical knowledge and provide a practical understanding of deploying AI techniques within computer vision. The main goal is to equip students with a robust skill set that translates into practical acumen, preparing a competent workforce to navigate and innovate in the complex landscape of Industry 4.0. This approach emphasizes the criticality of adapting educational strategies to meet the evolving demands of advanced technological infrastructures. It ensures that emerging professionals are adept at harnessing the potential of transformative tools like computer vision in industrial settings.

Dataset

This dataset was created by Vladimir Zhuravlev

Contents

Figshare Yolov4tiny

## Overview

Figshare Yolov4tiny is a dataset for object detection tasks - it contains Brain Tumor annotations for 1,973 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Install TAO Toolkit using pip and make sure to pull its docker container with GPU runtime (if using Colab or similar service) otherwise, the operation will fail. It is a relatively large image (21GB) as a result it will a while to download. Training YoloV4 Tiny with widerface dataset using Nvidia TAO toolkit. TAO Yolov4 Tiny requires the input image shape to be a multiple of 32 therefore, the images were resized to 768 x 768 and were also converted to PNG format. Could not find the pretrained… See the full description on the dataset page: https://huggingface.co/datasets/tahirishaq10/widerface_kitti.

ABSTRACT Coffee farmers do not have efficient tools to have sufficient and reliable information on the maturation stage of coffee fruits before harvest. In this study, we propose a computer vision system to detect and classify the Coffea arabica (L.) on tree branches in three classes: unripe (green), ripe (cherry), and overripe (dry). Based on deep learning algorithms, the computer vision model YOLO (You Only Look Once), was trained on 387 images taken from coffee branches using a smartphone. The YOLOv3 and YOLOv4, and their smaller versions (tiny), were assessed for fruit detection. The YOLOv4 and YOLOv4-tiny showed better performance when compared to YOLOv3, especially when smaller network sizes are considered. The mean average precision (mAP) for a network size of 800 × 800 pixels was equal to 81 %, 79 %, 78 %, and 77 % for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny, respectively. Despite the similar performance, the YOLOv4 feature extractor was more robust when images had greater object densities and for the detection of unripe fruits, which are generally more difficult to detect due to the color similarity to leaves in the background, partial occlusion by leaves and fruits, and lighting effects. This study shows the potential of computer vision systems based on deep learning to guide the decision-making of coffee farmers in more objective ways.

New_TSD YOLOv4_tiny

## Overview

New_TSD YOLOv4_tiny is a dataset for object detection tasks - it contains Traffic Signs annotations for 253 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).

FINAL_YOLOv4_tiny

## Overview

FINAL_YOLOv4_tiny is a dataset for object detection tasks - it contains Traffic annotations for 9,470 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).

This upload contains the following:

The training data of 40 pallets and the resulting weights for four YOLOv4-tiny models, two trained to detect pallets and two trained to detect pallet blocks

The documentation of 280 flights using these four models as a means of navigation for a micro drone, in the form of logs, images and feature vectors

The resultings evaluation and visualisation scripts

Further details, documentation and information on the project can be found in the corresponding Github Repo and publication. If you have any questions concerning these datasets, feel free to contact the corresponding author, Jérôme Rutinowski.

This work is part of the project "Silicon Economy Logistics Ecosystem" which is funded by the German Federal Ministry of Transport and Digital Infrastructure.

To meet the goals of computer vision-based understanding of images adopted in agriculture for improved fruit production, it is expected of a recognition model to be robust against complex and changeable environment, fast, accurate and lightweight for a low power computing platform deployment. For this reason, a lightweight YOLOv5-LiNet model for fruit instance segmentation to strengthen fruit detection was proposed based on the modified YOLOv5n. The model included Stem, Shuffle_Block, ResNet and SPPF as backbone network, PANet as neck network, and EIoU loss function to enhance detection performance. YOLOv5-LiNet was compared to YOLOv5n, YOLOv5-GhostNet, YOLOv5-MobileNetv3, YOLOv5-LiNetBiFPN, YOLOv5-LiNetC, YOLOv5-LiNet, YOLOv5-LiNetFPN, YOLOv5-Efficientlite, YOLOv4-tiny and YOLOv5-ShuffleNetv2 lightweight model including Mask-RCNN. The obtained results show that YOLOv5-LiNet having the box accuracy of 0.893, instance segmentation accuracy of 0.885, weight size of 3.0 MB and real-time detection of 2.6 ms combined together outperformed other lightweight models. Therefore, the YOLOv5-LiNet model is robust, accurate, fast, applicable to low power computing devices and extendable to other agricultural products for instance segmentation.

The aim of this project to develope a Yolo model that capable of detect and tracking obstacle for Drone/UAVs application. It only has one class which is "Obstacle". It consists everthing that the Drone/UAV expected to face at targeted test location.

There are three test environment that I want to test it in, include: 1. Urban area and city center, with multiple object to be detect and track 2. Jungle or forest, with main goal is for the drone to navigate itself through the trees and bushes 3. Random location, daily environment such as park and recreation center

Yolo version to be trained on: - Yolov5s - Yolov4-tiny - Yolov7-tiny

"Wish me luck" -Afif Mazlin

Cardboard_Training

## Overview

Cardboard_Training is a dataset for object detection tasks - it contains Cardboard Object annotations for 204 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).

BillarMini

## Overview

BillarMini is a dataset for object detection tasks - it contains Billarmini annotations for 304 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).