Train The Yolov11 On Pests

## Overview

Train The Yolov11 On Pests is a dataset for object detection tasks - it contains Pests annotations for 8,518 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).

YOLOV11 TRAINING DIAZ

## Overview

YOLOV11 TRAINING DIAZ is a dataset for object detection tasks - it contains Eyes Mouth Titles annotations for 310 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).

Training Yolov11 Coba Coba

## Overview

Training Yolov11 Coba Coba is a dataset for object detection tasks - it contains Eyes Mouth Titles annotations for 310 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).

YOLOV11 PAGI

## Overview

YOLOV11 PAGI is a dataset for object detection tasks - it contains Plastics annotations for 5,890 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).

Eagle Dataset - YOLOv11 OBB for Vehicle Detection

High-resolution aerial imagery with 16,000+ oriented bounding boxes for vehicle detection, pre-formatted for Ultralytics YOLOv11.

Context

This dataset is a ready-to-use version of the original Eagle Dataset from the German Aerospace Center (DLR). The original dataset was created to benchmark object detection models on challenging aerial imagery, featuring vehicles at various orientations.

This version has been converted to the YOLOv11-OBB (Oriented Bounding Box) format. The conversion makes the dataset directly compatible with modern deep learning frameworks like Ultralytics YOLO, allowing researchers and developers to train state-of-the-art object detectors with minimal setup.

The dataset is ideal for tasks requiring precise localization of rotated objects, such as vehicle detection in parking lots, traffic monitoring, and urban planning from aerial viewpoints.

Content

The dataset is split into training, validation, and test sets, following a standard structure for computer vision tasks.

Dataset Split & Counts:

• Training Set: 159 images and labels
• Validation Set: 53 images and labels
• Test Set: 106 images and labels

Directory Structure:

EagleDatasetYOLO/
├── train/
│  ├── images/   # 159 images
│  └── labels/   # 159 .txt obb labels
├── val/
│  ├── images/   # 53 images
│  └── labels/   # 53 .txt obb labels
├── test/
│  ├── images/   # 106 images
│  └── labels/   # 106 .txt obb labels
├── data.yaml
└── license.md

Annotation Format (YOLOv11-OBB):

Each .txt label file contains one object per line. The format for each object is: <class_id> <x_center> <y_center> <width> <height> <angle>

• <class_id>: The class index (in this case, 0 for 'vehicle').
• <x_center> <y_center>: The normalized center coordinates of the bounding box.
• <width> <height>: The normalized width and height of the bounding box.
• <angle>: The rotation angle of the box in radians, from -π/2 to π/2.

data.yaml Configuration:

A data.yaml file is included for easy integration with the Ultralytics framework.

path: ../EagleDatasetYOLO
train: train/images
val: val/images
test: test/images

nc: 1
names: ['vehicle']

Acknowledgements and License

This dataset is a conversion of the original work by the German Aerospace Center (DLR). The conversion to YOLOv11-OBB format was performed by Mridankan Mandal.

The dataset is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (CC BY-NC-SA 4.0).

If you use this dataset in your research, please cite the original creators and acknowledge the conversion work.

Yolo V11 Testing

## Overview

Yolo V11 Testing is a dataset for object detection tasks - it contains Objects annotations for 646 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 V 11 Training

## Overview

Yolo V 11 Training is a dataset for object detection tasks - it contains P annotations for 310 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).

🧃 Iranian Snack & Chips Detection Dataset (YOLO Format) This dataset contains annotated images of popular Iranian supermarket snacks and chips collected and labeled for object detection and instance segmentation tasks. It features 19 different product classes from well-known brands like Ashi Mashi, Cheetoz, Maz Maz, Naderi, Minoo, and Lina.

Perfect for training and evaluating YOLO models — including YOLOv5, YOLOv8, YOLOv11, and YOLOv12 — on real-world packaged goods in retail environments.

📁 Dataset Structure:

train/ – Training images valid/ – Validation images test/ – Test images data.yaml – Configuration file for YOLO models

Annotations are in YOLO format (polygons)

🧠 Classes (19 Total):

['Ashi Mashi snacks', 'Chee pellet ketchup', 'Chee pellet vinegar', 'Cheetoz chili chips', 'Cheetoz ketchup chips', 'Cheetoz onion and parsley chips', 'Cheetoz salty chips', 'Cheetoz snack 30g', 'Cheetoz snack 90g', 'Cheetoz vinegar chips', 'Cheetoz wheelsnack', 'Maz Maz ketchup chips', 'Maz Maz potato sticks', 'Maz Maz salty chips', 'Maz Maz vinegar chips',

'Mini Lina', 'Minoo cream biscuit', 'Naderi mini cookie', 'Naderi mini wafer']

🔧 Recommended Use Cases:

Product recognition in retail and supermarket scenes Fine-tuning YOLO models for regional or branded goods Instance segmentation of snacks and chips

Dataset for research on real-world consumer product detection

📎 Source & License:

Annotated with Roboflow License: CC BY 4.0 – Free to use, modify, and redistribute with attribution Created by: Hamed Mahmoudi (halfbloodhamed)

The rapid development of modern industry has significantly raised the demand for workpieces. To ensure the quality of workpieces, workpiece surface defect detection has become an indispensable part of industrial production. Most workpiece surface defect detection technologies rely on cloud computing. However, transmitting large volumes of data via wireless networks places substantial computational burdens on cloud servers, significantly reducing defect detection speed. Therefore, to enable efficient and precise detection, this paper proposes a workpiece surface defect detection method based on YOLOv11 and edge computing. First, the NEU-DET dataset was expanded using random flipping, cropping, and the self-attention generative adversarial network (SA-GAN). Then, the accuracy indicators of the YOLOv7–YOLOv11 models were compared on NEU-DET and validated on the Tianchi aluminium profile surface defect dataset. Finally, the cloud-based YOLOv11 model, which achieved the highest accuracy, was converted to the edge-based YOLOv11-RKNN model and deployed on the RK3568 edge device to improve the detection speed. Results indicate that YOLOv11 with SA-GAN achieved mAP@0.5 improvements of 7.7%, 3.1%, 5.9%, and 7.0% over YOLOv7, YOLOv8, YOLOv9, and YOLOv10, respectively, on the NEU-DET dataset. Moreover, YOLOv11 with SA-GAN achieved an 87.0% mAP@0.5 on the Tianchi aluminium profile surface defect dataset, outperforming the other models again. This verifies the generalisability of the YOLOv11 model. Additionally, quantising and deploying YOLOv11 on the edge device reduced its size from 10,156 kB to 4,194 kB and reduced its single-image detection time from 52.1ms to 33.6ms, which represents a significant efficiency enhancement.

D-Fire Dataset for Smoke and Fire Detection

This dataset is an enhanced version of the original D-Fire dataset, designed to facilitate smoke and fire detection tasks. It has been restructured to include a validation split, making it more accessible and user-friendly.

Explore Flare Guard

Introducing Flare Guard — an advanced, open-source solution for real-time fire and smoke detection.

This system uses YOLOv11, an advanced object detection model, to monitor live video feeds and detect fire hazards in real-time. Detected threats trigger instant alerts via Telegram and WhatsApp for rapid response.

🔗 Quick Access Links

• 📂 GitHub Repository – Access the full source code and documentation.
• 🎥 LinkedIn Project Demo & Launch – Watch the live demonstration and learn more about the project.

Example of reached Results:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12748471%2F632cfe5056cc683123c1873547d670ce%2Falert_20250210-034709-167281.jpg?generation=1742122420748481&alt=media" alt="CCVT_EXAMPLE">

Directory Structure

The dataset is organized as follows:

• train/
  • images/: Training images
  • labels/: Training labels in YOLO format
• val/
  • images/: Validation images
  • labels/: Validation labels in YOLO format
• test/
  • images/: Test images
  • labels/: Test labels in YOLO format

Classes

The dataset includes annotations for the following classes:

• 0: Smoke
• 1: Fire

Dataset Statistics

The dataset comprises over 21,000 images, categorized as follows:

Total bounding boxes:

• Fire: 14,692
• Smoke: 11,865

Data Splits

The dataset is divided into training, validation, and test sets to support model development and evaluation.

Citation

If you use this dataset in your research or projects, please cite the original paper:

Pedro Vinícius Almeida Borges de Venâncio, Adriano Chaves Lisboa, Adriano Vilela Barbosa. "An automatic fire detection system based on deep convolutional neural networks for low-power, resource-constrained devices." Neural Computing and Applications, vol. 34, no. 18, 2022, pp. 15349–15368. DOI: 10.1007/s00521-022-07467-z.

Acknowledgments

Credit for the original dataset goes to the researchers from Gaia, solutions on demand (GAIA). The original dataset and more information can be found in the D-Fire GitHub repository.

Risk Detection

Dataset for Training YOLOv11 AI Model to Detect Risky Objects for Assistive Guidance.

Objective

The goal of this project is to create a comprehensive dataset to train an AI model using YOLOv11 (You Only Look Once version 11). The model will detect and identify "risky objects" that blind and visually impaired individuals may encounter in indoor and outdoor environments. This dataset serves as the foundation for an assistive technology tool designed to enhance mobility and safety by providing real-time object detection and guidance.

Dataset Overview

Targeted Objects

Objects identified as potentially risky were selected through research and user studies. The dataset focuses on items that could obstruct paths, pose tripping hazards, or cause injury if unnoticed.

Examples include:
- Outdoor Risks:
* Vehicles
* Bicycles
* Potholes
* Curbs
* Barriers
* People

• Indoor Risks:
  • Chairs
  • Tables
  • Shelves
  • Furniture (e.g., cabinet, closet)

Key Features of the Dataset

• Comprehensive Annotations:
  Every image is annotated with bounding boxes and labeled with object categories.
• Diverse Scenarios:
  Images are captured under varied lighting conditions, environments (urban, suburban, rural), and angles to ensure robustness.

Dataset Structure

1. Total Images: x labeled images.
2. Categories: x object classes identified as risky.
3. Formats: Compatible with YOLO input formats and others.

Benefits of the AI Model

Real-Time Hazard Detection

The YOLOv11 model will process visual data from a wearable or smartphone camera, identifying and alerting the user to risks in real-time.

Improved Independence

By providing proactive guidance, the system empowers blind and visually impaired individuals to navigate more independently and safely.

Technical Details

Model Training

• YOLOv11 architecture optimized for high accuracy and real-time performance.
• The dataset will be split into:
  1. Training (70%)
  2. Validation (20%)
  3. Testing (10%)

Augmentation Techniques

• Data Augmentation:
  Techniques such as rotation, scaling, and brightness adjustments to increase dataset robustness.
• Simulated Obstacles:
  Simulations of real-world obstacles (e.g., occlusion, partial object visibility).

Evaluation Metrics

• Precision
• Recall
• F1-Score
• mAP (mean Average Precision)

Conclusion

This project aims to leverage advanced AI technology to address the unique challenges faced by blind and visually impaired individuals. By creating a specialized dataset for training YOLOv11, the model can detect risky objects with high precision, enhancing safety and mobility. The ultimate outcome is an AI-powered assistive system that provides greater independence and confidence to its users in their everyday lives.

Credits and Acknowledgments

This project incorporates images from the following public datasets. We extend our gratitude to the creators and contributors of these datasets for making their work freely available to the research community:

1. Dataset Name 1
   • Description of the dataset (e.g., type of images, purpose).
   • License: CC BY 4.0.

We adhere to the terms and conditions of these datasets' licenses and greatly appreciate their contribution to advancing research in AI and assistive technologies.

This dataset consists of couple of hundreds 640x640 images of target-plates used for shooting sports. It consists of 2 classes:

plate -- intact plate

shards -- debris of plate that was shot.

Annotations are in yolov11 format Some images in train are augmented versions of already presented images. These frames are originated from these videos: https://drive.google.com/file/d/1zVipKLgb5qskRcepYHrb8Yddu0ATlHRO/view?usp=sharing password: ITMO/HSE-MLBD-LW-3

The BRA-Dataset is an expanded dataset of Brazilian wildlife, developed for object detection tasks, combining real images with synthetic samples generated by Generative Adversarial Networks (GANs). It includes five medium- and large-sized mammal species frequently involved in roadkill incidents on Brazilian highways: lowland tapir (Tapirus terrestris), jaguarundi (Herpailurus yagouaroundi), maned wolf (Chrysocyon brachyurus), puma (Puma concolor), and giant anteater (Myrmecophaga tridactyla). The primary goal is to provide a comprehensive and standardized resource for biodiversity conservation research, wildlife monitoring technologies, and computer vision applications, with an emphasis on automated wildlife detection.

The original dataset by Ferrante et al. (2022) was built from images of wildlife captured through camera traps, field cameras, and structured internet searches, followed by manual curation and bounding box annotation. In this work, the dataset was expanded to approximately 9,238 images, divided into three main groups:

1. Real images — original photographs collected from the aforementioned sources. Total: 1,823.

2. Images augmented by classical techniques — generated from real images using transformations such as rotations (RT), horizontal flips (HF), vertical flips (VF), and horizontal (HS) and vertical shifts (VS). Total: 7,300.

3. Synthetic images generated by GANs — produced with WGAN-GP models trained individually for each species, using pre-processed image subsets. All generated samples underwent qualitative assessment to ensure morphological consistency, proper framing, and visual fidelity before inclusion. Total: 115.

The directory structure is organized into images/ and labels/, each subdivided into train/ and val/, following an 80% training and 20% validation split. Images are provided in .jpg format and annotations in .txt following the YOLO standard (class_id x_center y_center width height, with normalized coordinates). Furthermore, the file naming convention is designed to clearly indicate the species and the type of data augmentation applied.

The dataset is compatible with various object detection architectures and was evaluated using YOLOv5, YOLOv8, and YOLOv11 in n, s, and m variants, aiming to assess the impact of dataset expansion in scenarios with different computational capabilities and performance requirements.

By combining real data, classical augmentations, and high-quality synthetic samples, the BRA-Dataset provides a valuable resource for wildlife detection, environmental monitoring, and conservation research, especially in contexts where image availability for rare or threatened species is limited.

Falls pose a significant health risk for elderly populations, necessitating advanced monitoring technologies. This study introduces a novel two-stage fall detection system that combines computer vision and machine learning to accurately identify fall events. The system uses the YOLOv11 object detection model to track individuals and estimate their body pose by identifying 17 key body points across video frames. The proposed approach extracts nine critical geometric features, including the center of mass and various body angles. These features are used to train a support vector machine (SVM) model for binary classification, distinguishing between standing and lying with high precision. The system’s temporal validation method analyzes sequential frame changes, ensuring robust fall detection. Experimental results, evaluated on the University of Rzeszow Fall Detection (URFD) dataset and the Multiple Cameras Fall Dataset (MCFD), demonstrate exceptional performance, achieving 88.8% precision, 94.1% recall, an F1-score of 91.4%, and a specificity of 95.6%. The method outperforms existing approaches by effectively capturing complex geometric changes during fall events. The system is applicable to smart homes, wearable devices, and healthcare monitoring platforms, offering a scalable, reliable, and efficient solution to enhance safety and independence for elderly individuals, thereby contributing to advancements in health-monitoring technology.

Objective This project focuses on developing an object detection model using the YOLOv11 architecture. The primary goal is to accurately detect and classify objects within images across three distinct classes. The model was trained for 250 epochs to achieve high performance in terms of mean Average Precision (mAP), Precision, and Recall.

Dataset Information - Number of Images: 300 - Number of Annotations: 582 - Classes: 3 - Average Image Size: 0.30 megapixels - Image Size Range: 0.03 megapixels to 11.83 megapixels - Median Image Ratio: 648x500 pixels

Dataset Split

• Train Set: 90% (270 images)
• Validation Set: 8% (24 images)
• Test Set: 2% (6 images)

Preprocessing - Auto-Orient: Applied to ensure correct image orientation. - Resize: Images were stretched to a uniform size of 640x640 pixels to maintain consistency across the dataset. Augmentations - Outputs per Training Example: 3 augmented outputs were generated for each training example to enhance the diversity of the training data. - Crop: Random cropping was applied with a minimum zoom of 0% and a maximum zoom of 8%. - Rotation: Images were randomly rotated between -8° and +8° to improve the model's robustness to different orientations.

Training and Performance The model was trained for 250 epochs, and the following performance metrics were achieved: - mAP (mean Average Precision): 90.4% - Precision: 87.7% - Recall: 83.4%

These metrics indicate that the model is highly effective in detecting and classifying objects within the images, with a strong balance between precision and recall.

** Key Insights** - mAP: The high mAP score of 90.4% suggests that the model is accurate in predicting the correct bounding boxes and class labels for objects in the dataset. - Precision: A precision of 87.7% indicates that the model has a low false positive rate, meaning it is reliable in identifying true objects. - Recall: The recall of 83.4% shows that the model is capable of detecting most of the relevant objects in the images. Visualization The training process was monitored using various metrics, including mAP, Box Loss, Class Loss, and Object Loss. The visualizations show the progression of these metrics over the 250 epochs, demonstrating the model's learning and improvement over time.

Conclusion The project successfully implemented and trained an object detection model using the YOLOv11 architecture. The achieved performance metrics highlight the model's effectiveness and reliability in detecting objects across different classes. This model can be further refined and applied to real-world applications for object detection tasks.

AlgoLayout-YOLO

AlgoLayout-YOLO trains a YOLOv11 model to detect five algorithm-centric layout elements in IEEE computer-vision / AI papers:

1. AlgorithmBlock – pseudocode boxes headed by “Algorithm N:” (with Input / Output lines).

2. MethodFlowchart – block-and-arrow diagrams that visualise the overall workflow or model architecture.

3. BenchmarkTable – quantitative comparison tables listing performance across algorithms, datasets, or settings.

4. QualitativeFigure – side-by-side “before vs. after” images showing visual results.

5. AuthorProfile – author head-shot paired with name, email, and affiliation text blocks.

tennis shot analysis for serve, forehand, backhand, and ready position shots type. with yolov11 model

In this Dataset I annotated the Cow's Neck for train the model for detect the Neck.I trained it with Yolov11 and I got 92.2% Precision confidence.