This is my first deep learning project using YOLOv5. In this project I made deep learning model to detect 2 kind of flower in my house yard. There are 2 types of flowers (hortesia and mavavicus). In total I used 104 images that I took and labelled by my self.

YOLOv8 is the latest installment in the highly influential family of models that use the YOLO (You Only Look Once) architecture. YOLOv8 was developed by Ultralytics, a team known for its work on YOLOv3 and YOLOv5.

a YOLOv3 implementation to detect flowers based on existing https://github.com/ultralytics/yolov3. Resources

Mar 1, 2024 · Our study focuses on the application and comparison of deep learning methods including YOLO architectures for flower detection, and multi-column convolutional neural networks for density map prediction and counting in highly populated images.

Sep 1, 2021 · To achieve flower image classification, this paper proposes a deep learning method using the current powerful object detection algorithm YOLOv5 to achieve fine-grained image classification of flowers.

Jan 1, 2024 · To address the challenges resulting from the small size, similar colour, and overlapping occlusion of strawberry seedling flowers and fruits, this paper proposes a real-time recognition algorithm (Improved YOLO) for accurately identifying them.

The Nature3 dataset provides high-quality, annotated images for detecting and classifying plant leaves, flowers, and fruits. Designed for use with YOLO object detection models, it supports real-time detection applications in agriculture, ecological research, and plant identification.

Jun 7, 2022 · This study proposed an improved YOLOv4 model called YOLO-PEFL model for accurate pear flower detection in the natural environment. Pear flower targets were artificially synthesized with pear flower’s surface features.

Abstract: In order to quickly and accurately identify the flowers that meet the picking requirements by using the flower picking robot in the natural environment, an intelligent flower detection system based on YOLOv5s was proposed. In this study, the collected data sets of roses in different flowering periods are taken as the object.

Oct 11, 2024 · The main goal is to develop a highly robust model, capable of automatically classifying flowers with the best possible F1-score (macro) on the test dataset. I need to use techniques such as data augmentations, transfer learning with PyTorch's timm library, YOLO for detections - Silvano315/Flower-Recognition-and-Detection-with-PyTorch