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).

Indian Driving Dataset-Detections (IDD-D) in YOLOv11 Format:

A comprehensive dataset for object detection, tailored for unique Indian road conditions.

Context

This dataset provides a version of the Indian Driving Dataset (IDD), specifically the Detections subset (IDD-D), that has been converted and optimized for training object detection models using the YOLO (You Only Look Once) framework.

Indian roads present a unique set of challenges for autonomous navigation and computer vision, including diverse vehicle types not seen elsewhere, chaotic traffic, and varying environmental conditions. This dataset was prepared to make it easier for researchers and developers to train models like YOLO on this valuable data without needing to perform the conversion and restructuring themselves.

The original data was collected and annotated by researchers at IIIT Hyderabad. This version has been cleaned, reorganized, and converted to the standard YOLO .txt format by Mridankan Mandal.

Content:

The dataset is structured for immediate use with modern YOLO training pipelines and contains 41,962 labeled images split into training, validation, and test sets.

Key Features: * Optimized for YOLO: Annotations are in the normalized class x_center y_center width height format. * Diverse and Relevant Classes: Features 15 object classes crucial for navigating Indian roads, including autorickshaw, rider, and animal. * Structured Splits: The data is pre-split into train, val, and test directories with an approximate 80/10/10 ratio. * Ready-to-Use: Includes a data.yaml file for easy integration into your training scripts.

File Structure:

The dataset is organized as follows: ```

IDDDetectionsYOLODataset/ ├── train/ │ ├── images/ (33,569 images) │ └── labels/ (33,569 .txt files) ├── val/ │ ├── images/ (4,196 images) │ └── labels/ (4,196 .txt files) ├── test/ │ ├── images/ (4,197 images) │ └── labels/ (4,197 .txt files) ├── data.yaml ├── license.md └── ReadMe.md

**Classes (15 total):**
`animal`, `autorickshaw`, `bicycle`, `bus`, `car`, `caravan`, `motorcycle`, `person`, `rider`, `traffic light`, `traffic sign`, `trailer`, `train`, `truck`, `vehicle fallback`

---

## Acknowledgements:

Full credit for the original data collection and annotation goes to the creators at IIIT Hyderabad. This dataset is a derivative work prepared by Mridankan Mandal.

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

```bibtex
@inproceedings{Varma_2019_WACV,
  author = {Varma, Girish and Subramanian, Anbumani and Namburi, Tejaswi and Namboodiri, Anoop M. and Sharma, Manuj M. and Jawahar, C.V.},
  title = {{IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments}},
  booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
  month = {January},
  year = {2019}
}

License

This dataset is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. It is intended for academic and research purposes only and may not be used for commercial applications.

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).

VSAI Aerial Vehicle Detection Dataset (YOLO OBB Format):

A cleaned, and reformatted version of the VSAI Dataset, specifically adapted for Oriented Bounding Box (OBB) vehicle detection using the YOLOv11 format.

Overview:

This dataset is designed for aerial/drone-based vehicle detection tasks. It is a modified version of the original VSAI Dataset v1 by the DroneVision Team. This version has been modified by Mridankan Mandal for the easy of training object detection models like the YOLO11-OBB models.

The dataset is split into two classes: small-vehicle and large-vehicle. All annotations have been converted to the YOLOv11-OBB format, and the data is organized into training, validation, and testing sets.

Key Features and Modifications:

This dataset improves upon the original by incorporating several key modifications to make it more accessible and useful for modern computer vision tasks:

• Format Conversion: The annotations have been converted to the YOLOv11-OBB format, which uses four corner points to define an oriented bounding box.
• Data Cleaning: All image and annotation pairs where the label file was empty have been removed to ensure dataset quality.
• Structured Splits: The dataset is pre-split into train (80%), validation (10%), and test (10%) sets, with the following image counts:
  • Train: 4,297 images
  • Validation: 537 images
  • Test: 538 images
  • Total: 5,372 images
• Coordinate Normalization: All bounding box coordinates are normalized to a range of [0.0 - 1.0], making them ready for training without preprocessing.

Directory Structure

The dataset is organized in a standard YOLO format for easy integration with popular training frameworks.

YOLOOBBVSAIDataset/
├── train/
│  ├── images/   #Contains 4,297 image files.
│  └── labels/   #Contains 4,297 .txt label files.
├── val/
│  ├── images/   #Contains 537 image files.
│  └── labels/   #Contains 537 .txt label files.
├── test/
│  ├── images/   #Contains 538 image files.
│  └── labels/   #Contains 538 .txt label files.
├── data.yaml    #Dataset configuration file.
├── license.md   #Full license details.
└── ReadMe.md    #Dataset README file.

Annotation Format:

Each .txt label file contains one or more lines, with each line representing a single object in the YOLOv11-OBB format:

class_id x1 y1 x2 y2 x3 y3 x4 y4

• class_id: An integer representing the object class (0 for small-vehicle, 1 for large-vehicle).
• (x1, y1)...(x4, y4): The four corner points of the oriented bounding box, with coordinates normalized between 0 and 1.

data.yaml:

To begin training a YOLO model with this dataset, you can use the provided data.yaml file. Simply update the path to the location of the dataset on your local machine.

#The path to the root dataset directory.
path: /path/to/YOLOOBBVSAIDataset/
train: train/images
val: val/images
test: test/images

#Number of classes.
nc: 2

#The Class names,
names:
 0: small-vehicle
 1: large-vehicle

License and Attribution:

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

• You are free to: Use, modify, and redistribute this dataset for non-commercial research and educational purposes.
• You must: Provide proper attribution to both the original creators and the modifier, and release any derivative works under the same license.

Proper Attribution:

When using this dataset, attribute as follows:

• Original VSAI Dataset v1 by DroneVision Team, licensed under CC BY-NC-SA 4.0.
• Modified VSAI Dataset (YOLOv11-OBB Format) by Mridankan Mandal, licensed under CC BY-NC-SA 4.0.

Citation:

If you use this dataset in your research, use the following BibTeX entry to cite it:

@dataset{vsai_yolo_obb_2025,
 title={VSAI Dataset (YOLOv11-OBB Format)},
 author={Mridankan Mandal},
 year={2025},
 note={Modified from original VSAI v1 dataset by DroneVision},
 license={CC BY-NC-SA 4.0}
}

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).

A clean, ready-to-train dataset derived from the Cityscapes benchmark focused solely on road segmentation using YOLOv11-compatible format. Ideal for autonomous driving, road detection, and scene understanding tasks.

This dataset includes: - Polished segmentation masks in YOLO polygon format - Split into train and Val sets - Supports YOLOv11 training out of the box

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.

Car Make Classification Dataset (Pakistan Roads)

  Overview

This dataset was developed to support car make classification on vehicles commonly found on Pakistani roads. It contains ~15,000 images across 49 classes, annotated and prepared through Roboflow. The dataset was used to train a YOLOv11-Nano classification pipeline, combining object detection and classification for real-time video analysis.

  Dataset Structure

Total images: ~15,000 Number of classes: 49… See the full description on the dataset page: https://huggingface.co/datasets/em4bm4lik/car-make-classification-pk.

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.

This is a data set for Bits N' Bytes, an AI powered smart cabinet, used to train the AI Model YOLOv11.

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.

This dataset contains the base test set used in the inital test/train/val split as well as additional leaf and other host data for all three models. To reproduce testing results, download the model weights here and run our custom inference testing script found here. You can also evaluate our models using the built in YOLOv11 validation function, but the performance metrics returned are limited. For YOLOv11 validation on Google Colab, the command looks like this:

!yolo task=detect mode=val model=detection_v209_5_class.pt data=/content/v209_five_class/data.yaml conf=0.5 imgsz=1024 seed=42

Designed for industrial safety applications, this dataset provides high-quality, well-annotated data focusing on the detection of Personal Protective Equipment (PPE) and is particularly suitable for the training and application of computer vision models. The dataset contains 2,286 images of 640×640 pixels and focuses on the detection of PPE, such as the wearing of helmets and reflective undershirts. The data comes from a variety of sources, including public platforms such as GitHub, Kaggle, and Roboflow, as well as real-life photographs from different scenarios, to ensure that the data is diverse and can be adapted to a variety of scenarios and applications. The dataset is labeled and categorized according to the official YOLO specification, and the data can be directly applied to mainstream object detection frameworks such as YOLOv8 and YOLOv11, making it an important resource for researchers, developers, and practitioners. This dataset can be used to improve industrial safety monitoring systems and enhance construction site safety.

Data were collected on 24 pigs that were video-monitored day and night under two contrasted conditions: thermoneutral (TN, 22°C) and Heat Stress (32°C). All pigs were housed individually and had free access to an automatic electronic feeder delivering pellets four times a day, and to water. After acquisition, videos were processed using YOLOv11, a real-time object detection algorithm object detector that uses a convolutional neural network (CNN), to extract the following behavioural traits: drinking, willingness to eat, lying down, standing up, moving around, curiosity towards the littermate housed in the neighbouring pen, and contact between the two animals (cuddling). A minute frequency basis was applied (each minute correspond to 150 frames processed) for a continuous period of 16 days, spanning the two different thermal conditions (9 days on TN, 6 days on HS, 1 day back to TN). The algorithm was first trained thanks to manual video analysis labelling at the individual scale. Consistency with the automatic electronic feeder’s data (also provided) was thoroughly checked. The dataset allows quantitative criterion to be analysed to decipher inter-individual differences in animal behaviour and their dynamic adaptation to heat stress. This dataset can be used to train any machine learning methods for behaviour prediction from videos in conventional growing pigs.

BRAGAN is a new dataset of Brazilian wildlife developed for object detection tasks, combining real images with synthetic samples generated by Generative Adversarial Networks (GANs). It focuses on 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). Its primary goal is to provide a standardized and expanded resource for biodiversity conservation research, wildlife monitoring technologies, and computer vision applications, with an emphasis on automated wildlife detection.

The dataset builds upon the original BRA-Dataset by Ferrante et al. (2022), which was constructed from structured internet searches and manually curated with bounding box annotations. However, while the BRA-Dataset faced limitations in size and variability, BRAGAN introduces a new stage of dataset expansion through GAN-based synthetic image generation, substantially improving both the quantity and diversity of samples. In its final version, BRAGAN comprises approximately 9,238 images, divided into three main groups:

Real images — original photographs from the BRA-Dataset. Total: 1,823.

Classically augmented images — transformations applied to real samples, including rotations (RT), horizontal flips (HF), vertical flips (VF), and horizontal (HS) and vertical shifts (VS). Total: 7,300.

GAN-generated images — synthetic samples created using WGAN-GP models trained separately for each species on preprocessed subsets of the original data. All generated images underwent visual inspection to ensure morphological fidelity and proper framing before inclusion. Total: 115.

The dataset follows an organized directory structure with images/ and labels/ folders, each divided into train/ and val/ subsets, following an 80–20 split. Images are provided in .jpg format, while annotations follow the YOLO standard in .txt files (class_id x_center y_center width height, with normalized coordinates). The file naming convention explicitly encodes the species and the augmentation type for reproducibility.

Designed to be compatible with multiple object detection architectures, BRAGAN has been evaluated on YOLOv5, YOLOv8, and YOLOv11 (variants n, s, and m), enabling the assessment of dataset expansion across different computational settings and performance requirements.

By combining real data, classical augmentations, and high-quality synthetic samples, the BRAGAN 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.

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.

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

Project Overview This project, Helmet and Number Plate Detection for Motorbike Safety, provides a dataset for detecting motorbike riders wearing helmets, riders without helmets, and the presence of number plates. The dataset can be used for road safety monitoring, automated enforcement, and toll access systems.

1. Classes: The dataset includes the following classes:

• Bike: Identifies motorcycles and two-wheelers.
• Helmet: Detects riders wearing helmets.
• No Helmet: Identifies riders without helmets.
• Number Plate: Detects the presence of a number plate on the vehicle.

1. Current Status: Training Complete: Using the YOLOv11 model, we trained with 20,000 images across 300 epochs to achieve high accuracy. Public and Open to Contribution: The dataset allows community contributions to continually improve label accuracy and expand the dataset.

2. External Resources: YOLOv11 Documentation