Is Augment

## Overview

Is Augment is a dataset for instance segmentation tasks - it contains Objects annotations for 1,352 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).

With Mosaic Augment

## Overview

With Mosaic Augment is a dataset for instance segmentation tasks - it contains Objects IAHj annotations for 1,680 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).

Augmentations

## Overview

Augmentations is a dataset for instance segmentation tasks - it contains Defects annotations for 3,600 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).

Fine_tune Augment

## Overview

Fine_tune Augment is a dataset for instance segmentation tasks - it contains Cars annotations for 560 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).

Overview

This dataset is derived from the [Cell Counting v5 dataset on Roboflow] (https://universe.roboflow.com/cell-counting-hapu2/cell-counting-so7h7 ).
The original dataset was provided in YOLOv8 object detection format.
We created binary masks suitable for UNet-based semantic segmentation tasks.

Additionally, we generated augmented images to increase dataset variability.

Dataset Composition

• Train/Valid/Test Splits
  Each split contains:

  • images/: Source images
  • labels/: YOLO annotation files (kept for reference)
  • masks_binary/: Binary masks for semantic segmentation

• Augmented Images

  • Directory: aug_inference_only/images/
  • Contains 105 augmented images generated from the original 35 images
  • No masks or labels are provided for these augmentations
  • Intended for inference/visualization only (not for training or evaluation)

Data Augmentation

Each of the 35 original images was augmented with 3 additional variations, resulting in 105 augmented images.

Augmentation methods include:
- Random rotation (−90° to 90°)
- Flipping (horizontal, vertical, both)
- Shifting and scaling
- Brightness/contrast adjustment
- Gaussian noise injection

Source

• Base dataset: Roboflow Cell Counting Dataset

License

CC BY 4.0 – This dataset can be shared and adapted with appropriate attribution.

YOLO V11 (Instance Segmentation) (increase) (2025.03)

## Overview

YOLO V11 (Instance Segmentation) (increase) (2025.03) is a dataset for instance segmentation tasks - it contains Cancer Cells YHMV 9vVp annotations for 8,801 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 contains 810 images of 12 different classes of food types. The dataset contains food that is generically found across the globe like Pizzas, Burgers, Fries, etc., and some food items that are geographically specific to India. Those include Idli, Vada, Chapathi, etc. In order for the Yolo model to recognize extremely generic items like fruits and common ingredients, the dataset was trained on Apples, Bananas, Rice, Tomatoes, etc. This dataset was created using roboflow's dataset creator present on the roboflow website. The data was augmented using roboflow's dataset augmentation methods like Flip 90 degrees and different ranges of saturation. The dataset can be used with YoloV5 and YoloV8 as well.

Precious Gemstone Identification

Description: This comprehensive dataset comprises annotated images of a diverse range of precious gemstones meticulously curated for gemstone identification tasks. With 87 classes of gemstones for classification unique varieties including Chalcedony Blue, Amber, Aventurine Yellow, Dumortierite, Pearl, Aventurine Green, and many others, this dataset serves as a valuable resource for training and evaluating machine learning models in gemstone recognition.

Gemstone Variety: The dataset encompasses a wide spectrum of precious gemstones, ranging from well-known varieties like Emerald, Ruby, Sapphire, and Diamond to lesser-known gems such as Benitoite, Larimar, and Sphene.

Dataset Split: Train Set: 92% (46404 images) Validation Set: 4% (1932 images) Test Set: 4% (1932 images)

Preprocessing: Images in the dataset have been preprocessed to ensure consistency and quality:

• Auto-Orient: Applied to correct orientation inconsistencies.
• Resize: Images are uniformly resized to 640x640 pixels.
• Tiling: Organized into a grid of 3 rows x 2 columns for efficient processing.

Augmentations: To enhance model robustness and generalization, each training example has been augmented with various transformations:

• Flip: Horizontal and Vertical flips are applied.
• Rotation: Random rotation between -15° and +15°.
• Shear: Horizontal and Vertical shearing with a range of ±10°.
• Saturation: Adjusted randomly between -15% and +15%.
• Brightness: Random brightness adjustment between -10% and +10%.

File Formats Available:

• COCO Segmentation: COCO (Common Objects in Context) Segmentation format is commonly used for semantic segmentation tasks. It provides annotations for object segmentation, where each object instance is labeled with a mask indicating its outline.
• COCO: COCO format is a widely used standard for object detection and instance segmentation tasks. It includes annotations for bounding boxes around objects, along with corresponding class labels and segmentation masks if applicable.
• TensorFlow : TensorFlow format typically refers to a data format compatible with TensorFlow, a popular deep learning framework. It often includes annotations in a format suitable for training object detection and segmentation models using TensorFlow.
• VOC: VOC (Visual Object Classes) format is a standard format for object detection and classification tasks. It includes annotations for bounding boxes around objects, along with class labels and metadata, following the PASCAL VOC dataset format.
• YOLOv8-obb: YOLOv8-obb format is specific to the YOLO (You Only Look Once) object detection model architecture. It typically includes annotations for object bounding boxes in YOLO format, where each bounding box is defined by its center coordinates, width, height, and class label.
• YOLOv9 Segmentation: YOLOv9 Segmentation format is tailored for semantic segmentation tasks using the YOLOv9 architecture. It provides annotations for pixel-wise segmentation masks corresponding to object instances, enabling accurate segmentation of objects in images.
• Server Benchmark: The Server Benchmark format is used for annotated images with bounding boxes for object detection tasks. Each annotation entry in the JSON-like structure contains details about a specific object instance within an image.

Disclaimer:

The images included in this dataset were sourced from various online platforms, primarily from minerals.net and www.rasavgems.com websites, as well as other online datasets. We have curated and annotated these datasets for the purpose of gemstone identification and made them available in different formats. We do not claim ownership of the original images, and we do not claim to own these images. Any trademarks, logos, or copyrighted materials belong to their respective owners.

Researchers, enthusiasts and developers interested in gemstone identification, machine learning, and computer vision applications will find this dataset invaluable for training and benchmarking gemstone recognition algorithms.

Raw_3_augment_segmentation

## Overview

Raw_3_augment_segmentation is a dataset for instance segmentation tasks - it contains Ctrl M1 M2 Er9D annotations for 3,600 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 article utilizes image segmentation methods in computer vision to create a high-quality multi-scale micro/nanofiber dataset. The YOLOv8-FD algorithm based on small object detection improvement is used to automatically detect the diameter of micro/nanofibers. The system can achieve measurement and automated preparation for micro/nano fiber with a diameter of 462 nm − 125 μm within an error of 2.95%, and with the increase of fiber diameter, the error gradually decreases. The optical imaging resolution of a single pixel in the system is 65.97 nm, and the average detection time is 9.6 ms. This work is suitable for high-precision real-time measurement and automatic precise preparation of micro/nano fibers. Figure 1 shows the network structure of micro/nanofiber diameter detection based on deep learning. Figure 2 shows the automatic preparation system of micro/nanofiber based on deep learning. Figure 3 shows the original image of Loss and mAP changing with epoch during the training process, where the opju file can be opened using Origin software. Figure 4 shows the visualization and results of the deep learning model. Figure 5 shows the comparison of the segmentation results of the original YOLOv8 and YOLOv8-FD micro/nano fiber images. Figure 6 shows the AFM scanning image of the micro/nano fiber. The ibw file can be opened using Gwyddion.

🩻 Scoliosis YOLOv5 — Annotated Spine X-ray Dataset

This dataset is a curated and preprocessed collection of spinal X-ray images for deep learning–based scoliosis and vertebra detection using YOLOv5, YOLOv8, or other object detection frameworks.

It contains high-quality annotated X-rays featuring multiple bounding boxes per image — each representing different spinal regions and conditions.

🧩 Dataset Configuration

train: scoliosis yolov5/train/images
val: scoliosis yolov5/valid/images
test: scoliosis yolov5/test/images

nc: 3
names: ['Vertebra', 'scoliosis spine', 'normal spine']

⚙️ Data Details

• Train Set: /train/images
• Validation Set: /valid/images
• Test Set: /test/images
• Total Classes: 3
• Annotations: YOLOv5 format (.txt with class, x_center, y_center, width, height)
• Image Format: .jpg / .png

Classes Description:

1. Vertebra — Individual vertebral structures localized across the spine.
2. Scoliosis Spine — Spinal X-rays with visible curvature or deformation.
3. Normal Spine — Straight, healthy spinal alignment with no abnormality.

🧠 Augmentations Applied

To improve model generalization and balance the dataset, the following augmentations were used:

• Random rotation
• Brightness and contrast adjustment
• Horizontal flipping
• Random zoom and cropping
• Gaussian noise injection

🎯 Use Cases

This dataset is ideal for:

• Scoliosis detection and classification
• Vertebra localization and segmentation
• Object detection model benchmarking (YOLOv5/YOLOv8)
• Transfer learning on medical image datasets
• Explainable AI research in healthcare

📊 Source

The dataset was processed and annotated using Roboflow, then refined and organized into YOLOv5 format for seamless training. Each image includes verified bounding boxes for vertebral and scoliosis regions.

Roboflow Project Link: 🔗 View on Roboflow (add your Roboflow link here)

🧾 License

CC BY 4.0 — Free to use, modify, and redistribute with proper attribution.

Raw_3_augment_segmentation 3

## Overview

Raw_3_augment_segmentation 3 is a dataset for instance segmentation tasks - it contains Ctrl M1 M2 Er9D L2KD annotations for 2,700 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).

Ultralytics YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.

We hope that the resources here will help you get the most out of YOLOv8. Please browse the YOLOv8 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!

Coco Augmented Seg 2

## Overview

Coco Augmented Seg 2 is a dataset for instance segmentation tasks - it contains Parking Space T5uH annotations for 769 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.

Train Augmented

## Overview

Train Augmented is a dataset for instance segmentation tasks - it contains Augmented annotations for 12,357 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).

Blood Stains Augmented

## Overview

Blood Stains Augmented is a dataset for instance segmentation tasks - it contains Bloodstains annotations for 856 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).

Simu Raw Augmented

## Overview

Simu Raw Augmented is a dataset for instance segmentation tasks - it contains Delamination GXCp annotations for 11,605 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).

Seaming Bag 03 04 25 Mark 3 No Augment

## Overview

Seaming Bag 03 04 25 Mark 3 No Augment is a dataset for instance segmentation tasks - it contains Bag Defect EMpu 3KfW annotations for 230 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).

TOMATO AUGMENTED DIVIDED

## Overview

TOMATO AUGMENTED DIVIDED is a dataset for instance segmentation tasks - it contains Noathing annotations for 986 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).

Cnc_augment

## Overview

Cnc_augment is a dataset for instance segmentation tasks - it contains Black Or Clack Or Pinhole annotations for 272 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).