ALL CONTENT IS DIRECTLY FROM THE GITHUB README

RF-DETR: SOTA Real-Time Object Detection Model

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RF-DETR is a real-time, transformer-based object detection model architecture developed by Roboflow and released under the Apache 2.0 license.

RF-DETR is the first real-time model to exceed 60 AP on the Microsoft COCO benchmark alongside competitive performance at base sizes. It also achieves state-of-the-art performance on RF100-VL, an object detection benchmark that measures model domain adaptability to real world problems. RF-DETR is comparable speed to current real-time objection models.

RF-DETR is small enough to run on the edge, making it an ideal model for deployments that need both strong accuracy and real-time performance.

Results

We validated the performance of RF-DETR on both Microsoft COCO and the RF100-VL benchmarks.

https://media.roboflow.com/rf-detr/charts.png" alt="rf-detr-coco-rf100-vl-8">

| Model | mAP

This dataset was originally created by David Lee. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/david-lee-d0rhs/american-sign-language-letters. * Using Computer Vision to Help Deaf and Hard of Hearing Communities

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

Github

## Overview

Github is a dataset for object detection tasks - it contains Projects annotations for 848 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 dataset was originally created by Yimin Chen. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/workspace-txxpz/underwater-detection.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

This dataset was originally created by Rinat Landman. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/lettucedetector/complete_dataset_0910.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

The dataset (Lego_Tracking folder) has been created manually by recording 12 videos by smartphone. Of these, 10 were designated for training and 2 for testing. The videos showcase conveyor belts transporting LEGO bricks, captured from various perspectives (top, front, and diagonal) to provide diverse viewpoints. The videos have been recorded in the AI laboratory of the Eötvös Loránd University (https://github.com/BahruzHuseynov/Object-Tracking-AI_Lab) and the dataset has been used to make a research about detection-segmentation-tracking pipeline to complete the AI laboratory work. The dataset includes videos of differing complexities, classified as "Overlapping," "Normal," or "Simple," with varying durations ranging from short to long shots. Additionally, the annotation of LEGO bricks was performed frame-by-frame using the RoboFlow web application (https://roboflow.com/).

In addition, you can go to the dataset which has been prepared by applying systematic sampling on training videos to train and validate YOLOv8 and RT-DETR models from ultralytics: https://www.kaggle.com/datasets/hbahruz/multiple-lego/data

Another dataset prepared can be used for the semantic segmentation: https://www.kaggle.com/datasets/hbahruz/lego-semantic-segmentation/

This dataset contains different animal categories such as: buffalo, capybara, cat, cow, deer, dog, elephant, flamingo, giraffe, jaguar, kangaroo, lion, parrot, penguin, rhino, sheep, tiger, turtle and zebra.

Most of the images can be found in existing datasets: https://github.com/freds0/capybara_dataset https://universe.roboflow.com/miguel-narbot-usp-br/capybara-and-animals/dataset/1 https://www.kaggle.com/datasets/hugozanini1/kangaroodataset?resource=download https://github.com/experiencor/kangaroo https://universe.roboflow.com/z-jeans-pig/kangaroo-epscj/dataset/1 https://cvwc2019.github.io/challenge.html# https://www.kaggle.com/datasets/biancaferreira/african-wildlife https://universe.roboflow.com/new-workspace-5kofa/elephant-dataset/dataset/6 https://universe.roboflow.com/nathanael-hutama-harsono/large-cat/dataset/1/images/?split=train https://universe.roboflow.com/giraffs-and-cows/giraffes-and-cows/dataset/1 https://universe.roboflow.com/turtledetector/turtledetector/dataset/2 https://www.kaggle.com/datasets/smaranjitghose/sea-turtle-face-detection https://universe.roboflow.com/fadilyounes-me-gmail-com/zebra---savanna/dataset/1 https://universe.roboflow.com/test-qeryf/yolov5-9snhq https://universe.roboflow.com/or-the-king/two-zebras https://universe.roboflow.com/wild-animals-datasets/zebra-images/dataset/2 https://universe.roboflow.com/zebras/zebras/dataset/2 https://universe.roboflow.com/v2-rabotaem-xkxra/zebras_v2/dataset/5 https://universe.roboflow.com/vijay-vikas-mangena/animal_od_test1/dataset/1 https://universe.roboflow.com/bdoma13-gmail-com/rhino_horn/dataset/7 https://universe.roboflow.com/rudtkd134-naver-com/finalproject2/dataset/2 https://universe.roboflow.com/the-super-nekita/cats-brofl/dataset/2 https://universe.roboflow.com/lihi-gur-arie/pinguin-object-detection/dataset/2 https://universe.roboflow.com/utas-377cc/penguindataset-4dujc/dataset/10 https://universe.roboflow.com/new-workspace-tdyir/penguin-clfnj/dataset/1 https://universe.roboflow.com/utas-wd4sd/kit315_assignment/dataset/7 https://universe.roboflow.com/jeonjuuniv/deer-hqp4i/dataset/1 https://universe.roboflow.com/new-workspace-hqowp/sheeps/dataset/1 https://universe.roboflow.com/ali-eren-altindag/sheepstest2/dataset/1 https://universe.roboflow.com/yaser/sheep-0gudu/dataset/3 https://universe.roboflow.com/ali-eren-altindag/mixed_sheep/dataset/1 https://universe.roboflow.com/pkm-kc-2022/sapi-birahi/dataset/2 https://universe.roboflow.com/ghostikgh/team1_cows/dataset/5 https://universe.roboflow.com/ml-dlq4x/liontrain/dataset/2 https://universe.roboflow.com/animals/lionnew/dataset/2 https://universe.roboflow.com/parrottrening/parrot_trening/dataset/1 https://universe.roboflow.com/uet-hi8bg/parrots-r4tfl/dataset/1 https://universe.roboflow.com/superweight/parrot_poop/dataset/5 https://www.kaggle.com/datasets/tarunbisht11/intruder-detection

From those datasets the images has been filtered (deleted objects of size smaller 32, images with dimension smaller than 320px has been deleted, images and labeled objects has been renamed). The rest of images has been labeled by me.

Laboro Tomato Github

## Overview

Laboro Tomato Github is a dataset for instance segmentation tasks - it contains Ripness Segmentation Laboro annotations for 804 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 [BY-NC-SA 4.0 license](https://creativecommons.org/licenses/BY-NC-SA 4.0).

This dataset, meticulously created by Jordan Bird, Leah Bird, Carrie Ijichi, Aurelie Jolivald, Salisu Wada, Kay Owa, and Chloe Barnes from Nottingham Trent University (United Kingdom), forms a critical part of the RF100 initiative, an Intel-sponsored project aimed at developing a new object detection benchmark to assess model generalizability.

Comprising a rich collection of bee detection images, this dataset is structured to challenge and refine AI models in object detection tasks. With 5,640 images in the training set (70%), 1,604 images in the validation set (20%), and 836 images in the test set (10%), it offers a comprehensive resource for model training, validation, and testing.

By participating in the RF100 benchmark, researchers and developers can contribute to advancing the field of AI while ensuring that models are robust, accurate, and capable of generalizing across diverse scenarios.

Explore the RF100 initiative and access more resources on https://github.com/roboflow-ai/roboflow-100-benchmark GitHub repository.

Source: https://universe.roboflow.com/roboflow-100/bees-jt5in

Vehicle Detection Dataset

This dataset is designed for vehicle detection tasks, featuring a comprehensive collection of images annotated for object detection. This dataset, originally sourced from Roboflow (https://universe.roboflow.com/object-detection-sn8ac/ai-traffic-system), was exported on May 29, 2025, at 4:59 PM GMT and is now publicly available on Kaggle under the CC BY 4.0 license.

Overview

• Purpose: The dataset supports the development of computer vision models for detecting various types of vehicles in traffic scenarios.
• Classes: The dataset includes annotations for 7 vehicle types:
  • Bicycle
  • Bus
  • Car
  • Motorbike
  • Rickshaw
  • Truck
  • Van
• Number of Images: The dataset contains 9,440 images, split into training, validation, and test sets:
  • Training: Images located in ../train/images
  • Validation: Images located in ../valid/images
  • Test: Images located in ../test/images
• Annotation Format: Images are annotated in YOLOv11 format, suitable for training state-of-the-art object detection models.
• Pre-processing: Each image has been resized to 640x640 pixels (stretched). No additional image augmentation techniques were applied.

Source and Creation

This dataset was created and exported via Roboflow, an end-to-end computer vision platform that facilitates collaboration, image collection, annotation, dataset creation, model training, and deployment. The dataset is part of the ai-traffic-system project (version 1) under the workspace object-detection-sn8ac. For more details, visit: https://universe.roboflow.com/object-detection-sn8ac/ai-traffic-system/dataset/1.

Usage

This dataset is ideal for researchers, data scientists, and developers working on vehicle detection and traffic monitoring systems. It can be used to: - Train and evaluate deep learning models for object detection, particularly using the YOLOv11 framework. - Develop AI-powered traffic management systems, autonomous driving applications, or urban mobility solutions. - Explore computer vision techniques for real-world traffic scenarios.

For advanced training notebooks compatible with this dataset, check out: https://github.com/roboflow/notebooks. To explore additional datasets and pre-trained models, visit: https://universe.roboflow.com.

License

The dataset is licensed under CC BY 4.0, allowing for flexible use, sharing, and adaptation, provided appropriate credit is given to the original source.

This dataset is a valuable resource for building robust vehicle detection models and advancing computer vision applications in traffic systems.

Plants Detection Github

## Overview

Plants Detection Github is a dataset for object detection tasks - it contains Leaves annotations for 2,340 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 dataset was exported via roboflow.com on June 7, 2023 at 11:52 AM GMT

Roboflow is an end-to-end computer vision platform that helps you * collaborate with your team on computer vision projects * collect & organize images * understand and search unstructured image data * annotate, and create datasets * export, train, and deploy computer vision models * use active learning to improve your dataset over time

For state of the art Computer Vision training notebooks you can use with this dataset, visit https://github.com/roboflow/notebooks

To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com

The dataset includes 4142 images. Trash-detection are annotated in COCO format.

The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 640x640 (Stretch)

The following augmentation was applied to create 3 versions of each source image: * 50% probability of horizontal flip * Random rotation of between -45 and +45 degrees * Random brigthness adjustment of between -20 and 0 percent * Random Gaussian blur of between 0 and 2 pixels

The following transformations were applied to the bounding boxes of each image: * Random exposure adjustment of between -32 and +32 percent

This dataset was originally created by Wojciech Przydział, Dorota Przydział, Magdalena Przydział-Mazur, Bartłomiej Mazur. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/underwaterpipes/underwater_pipes_orginal_pictures.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

This dataset was originally created by Omar Kapur, wwblodge, Ricardo Jenez, Justin Jeng, Jeffrey Day. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/omarkapur-berkeley-edu/livestalk.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

This dataset contains images of an artifical flower platform with different insects sitting on it or flying above it. All images were automatically recorded with the Insect Detect DIY camera trap, a hardware combination of the Luxonis OAK-1, Raspberry Pi Zero 2 W and PiJuice Zero pHAT for automated insect monitoring (bioRxiv preprint).

Classes

The following object classes were annotated in this dataset:

• wasp (mostly Vespula sp.)
• hbee (Apis mellifera)
• fly (mostly Brachycera)
• hovfly (various Syrphidae, e.g. Episyrphus balteatus)
• other (all Arthropods with insufficient occurences, e.g. various Hymenoptera, true bugs, beetles)
• shadow (shadows of the recorded insects)

View the Health Check for more info on class balance.

Versions

• v4 insect_detect_416_1class
  • squashed to square (aspect ratio 1:1)
  • downscaled to 416x416 pixel
  • all classes merged into one class ("insect")
• v5 insect_detect_raw_4K
  • original images in 4K resolution (3840x2160 pixel)
• v7 insect_detect_320_1class
  • squashed to square (aspect ratio 1:1)
  • downscaled to 320x320 pixel
  • all classes merged into one class ("insect")

Deployment

You can use this dataset as starting point to train your own insect detection models. Check the model training instructions for more information.

Open source Python scripts to deploy the trained models can be found at the insect-detect GitHub repo.

This dataset was originally created by Ruud Krinkels. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/thermal-imaging-0hwfw/flir-data-set.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

Teste Yolo Github

## Overview

Teste Yolo Github is a dataset for object detection tasks - it contains Cars annotations for 627 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).

UW_Garbage_Debris_Dataset is a collection of underwater images showing garbage and debris that have been discarded into the oceans. The dataset has been preprocessed using a technique called Dark Prior Channel, which enhances the contrast of the images, making it easier to detect and identify the debris.

Try Model on Roboflow Universe "https://universe.roboflow.com/neural-ocean/neural_ocean/model" target="_blank"> https://app.roboflow.com/images/try-model-badge.svg" alt="Try Neural Ocean Model on Roboflow Universe"> Download Dataset from Roboflow Universe "https://universe.roboflow.com/neural-ocean/neural_ocean" target="_blank"> https://app.roboflow.com/images/download-dataset-badge.svg" alt="Download Neural Ocean Dataset from Roboflow Universe">

GitHub Repo:- https://github.com/ShivamVadalia/Underwater-Waste-Detection-Using-YoloV8-And-Water-Quality-Assessment

🦴 Human Bone Fractures Multi-modal Image Dataset (HBFMID) 🦾

This dataset, the Human Bone Fractures Multi-modal Image Dataset (HBFMID), is a collection of medical images (X-ray and MRI) focused on detecting bone fractures in various parts of the human body. It's designed to support research in computer vision and deep learning for medical applications. 🧑‍⚕️💻

📄 Description

The HBFMID dataset contains a total of 1539 images of human bones, including both X-ray and MRI modalities. The dataset covers a wide range of bone locations, such as:

• Elbow
• Finger
• Forearm
• Humerus
• Shoulder
• Femur
• Shinbone
• Knee
• Hipbone
• Wrist
• Spinal cord
• Some healthy bones

The initial dataset consisted of 641 raw images (510 X-ray and 131 MRI). This raw data was then divided into three subsets:

• Training: 449 images
• Validation: 128 images
• Testing: 64 images

The images were carefully annotated to label the presence and location of fractures. ✍️

⚙️ Pre-processing

The following pre-processing steps were applied to the images:

• Auto-orientation: Ensuring consistent image orientation. 🔄
• Resizing: All images were resized to 640x640 pixels. 📏
• Contrast adjustments: Enhancing image clarity. ☀️

➕ Augmentation

To increase the dataset size and improve the robustness of machine learning models, various augmentation techniques were applied to the training set, resulting in approximately 1347 training images (449 x 3). The augmentation techniques included:

• Flip: Horizontal and Vertical (50% probability each) ↔️↕️
• Rotation: Random rotation between -5° and +5°. 🔄
• Shear: Random shear of ±2° horizontally and ±2° vertically. 📐
• Saturation: Random adjustment between -5% and +5%. 🌈
• Brightness: Random adjustment between -10% and +10%. 💡
• Zooming: 2%. 🔍

🛠️ Exported via Roboflow

This dataset was exported using Roboflow, an end-to-end computer vision platform that facilitates:

• Team collaboration on computer vision projects.
• Image collection and organization.
• Understanding and searching unstructured image data.
• Annotation and dataset creation.
• Model training and deployment.
• Active learning for continuous dataset improvement.

For state-of-the-art Computer Vision training notebooks compatible with this dataset, visit https://github.com/roboflow/notebooks. 🚀

Explore over 100,000 other datasets and pre-trained models on https://universe.roboflow.com. 🌍

🏷️ Annotation Format

Fractured bones in this dataset are annotated in YOLOv8 format, which is widely used for object detection tasks. 🎯

📚 Categories

Computer Vision, Medical Imaging, Deep Learning

🔗 Related Links

• Article citing this dataset: https://link.springer.com/article/10.1007/s10489-024-05588-7#citeas 📰

📜 Licence

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This means you are free to share and adapt the material for any purpose, even commercially, as long as you give appropriate credit, provide a link to the license, and indicate if changes were made. 📄

✍️ Citation

Parvin, Shahnaj (2024), “Human Bone Fractures Multi-modal Image Dataset (HBFMID)”, Mendeley Data, V1, doi: 10.17632/xwfs6xbk47.1

🏢 Institution

American International University Bangladesh 🇧🇩

Context

Highly inspired by RAIN MAN 2.0 by Edje Electronics (https://www.youtube.com/watch?v=Nf3zBJ2cDAs) I wanted to create my own playing cards AI for Blackjack and Poker. I didn't have much prior experience to DL so I wanted to jump in straight away and train my own yolov5 model but there was no dataset provided, only the code for generating it.

Content

First I took 20-30 second videos of all 52 cards under variable light temperature and brightness. The images were processed with open-cv. The DTD dataset (https://www.robots.ox.ac.uk/~vgg/data/dtd/) was used to simulate backgrounds of various textures for our dataset.

The original generated dataset was in Pascal VOC format. It was uploaded to Roboflow and exported to YOLO v5 PyTorch format.

Images are 416x416, split into train/test/valid (70/20/10 split).

Use kaggle_data.yaml if training in kaggle; data.yaml if training on your local machine

Acknowledgements

Thanks to @geaxgx for providing a well documented jupyter notebook on generating this dataset. https://github.com/geaxgx/playing-card-detection