The Segment Anything Model (SAM) and its successor, SAM2, are highly influential foundation models in the field of computer vision, specifically designed for promptable image segmentation.

What SAM Is

• Foundation Model for Segmentation: SAM is a large, general-purpose model trained on a massive dataset to perform image segmentation. Its goal is to segment any object in any image, even those it hasn't seen before.
• Prompt-Based Segmentation: Its core feature is the ability to generate a high-quality segmentation mask based on a minimal user "prompt." These prompts can include:
  • Clicks: The user clicks a point on an object, and SAM generates the mask for that entire object.
  • Bounding Boxes: The user draws a box around an area, and SAM refines it into a precise mask.
  • Text or Scribbles: (In more advanced versions like SAM2) The model can generate masks based on more complex inputs.

Context in the Forgery Detection Competition

In the context of the Kaggle competition, the use of SAM/SAM2 is for Candidate Generation:

• The Problem: The competition, "Recod.ai/LUC - Scientific Image Forgery Detection," requires models to not only detect a forgery (like a copy-move manipulation in a biomedical image) but also to segment the manipulated region at the pixel level.
• SAM/SAM2's Role: SAM is a powerful tool for generating segmentation masks for any prominent object or region in an image. The notebook you linked, "Forgery using SAM2 for Candidate Generation," leverages this ability to:
  1. Run SAM2 over the forged images to automatically generate a large collection of general segmentation masks (the "candidates").
  2. The idea is that the actual forged regions, which often involve copying and pasting distinct objects or image patches, are likely to be contained within or closely align with one of these automatically generated SAM/SAM2 masks.
• Aiding Forgery Detection: By using SAM2, the notebook transforms the problem from searching for a forgery anywhere in the image to classifying and refining a smaller, more manageable set of high-quality object masks. This can significantly simplify the task for the final forgery-specific model.

Sam2 Instance Seg

## Overview

Sam2 Instance Seg is a dataset for instance segmentation tasks - it contains Building annotations for 275 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).

Sam2 Yolo11

## Overview

Sam2 Yolo11 is a dataset for instance segmentation tasks - it contains Beet Carrot annotations for 335 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).

godoldol99/SAM2-based-plant-disease-lesion-segmentation-model-DATASET dataset hosted on Hugging Face and contributed by the HF Datasets community

Few-Shot Segmentation (FSS) aims to learn class-agnostic segmentation on few classes to segment arbitrary classes, but at the risk of overfitting. To address this, some methods use the well-learned knowledge of foundation models (e.g., SAM) to simplify the learning process. Recently, SAM 2 has extended SAM by supporting video segmentation, whose class-agnostic matching ability is useful to FSS. A simple idea is to encode support foreground (FG) features as memory, with which query FG features are matched and fused. Unfortunately, the FG objects in different frames of SAM 2's video data are always the same identity, while those in FSS are different identities, i.e., the matching step is incompatible. Therefore, we design Pseudo Prompt Generator to encode pseudo query memory, matching with query features in a compatible way. However, the memories can never be as accurate as the real ones, i.e., they are likely to contain incomplete query FG, but some unexpected query background (BG) features, leading to wrong segmentation. Hence, we further design Iterative Memory Refinement to fuse more query FG features into the memory, and devise a Support-Calibrated Memory Attention to suppress the unexpected query BG features in memory. Extensive experiments have been conducted on PASCAL-5i and COCO-20i to validate the effectiveness of our design, e.g., the 1-shot mIoU can be 4.2% better than the best baseline.

Visual comparison of 100 human annotations (labels) compared with Segment Anything Model 2 (SAM2) segmentation.

SAM2 Labelling

## Overview

SAM2 Labelling is a dataset for instance segmentation tasks - it contains Bleeding Crakcs annotations for 1,916 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).

The dataset contains:

.
├── images
├── masks
└── train.csv

• Images – This folder contains 588 RGB images showcasing various types of leaf diseases.
• Masks – This folder holds 588 RGBA segmentation masks, where the diseased regions of the leaves are annotated.
• train.csv – A CSV file that maps each image to its corresponding segmentation mask, ensuring proper indexing for SAM2 training.

Original Dataset - kaggle.com/datasets/sovitrath/leaf-disease-segmentation

Sam2 Vegatables

## Overview

Sam2 Vegatables is a dataset for instance segmentation tasks - it contains Vegatable 10 annotations for 319 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).

VIRESET

VIRESET is a high-quality video instance editing dataset that provides temporally consistent and precise instance masks. Built upon the foundation of SA-V, VIRESET leverages the pretrained SAM-2 model to enhance the mask annotations from 6 FPS to 24 FPS, further enriched with detailed prompt-based annotations using PLLaVA. This dataset is used in the paper VIRES: Video Instance Repainting with Sketch and Text Guidance. Project page Code: https://github.com/suimuc/VIRES The… See the full description on the dataset page: https://huggingface.co/datasets/suimu/VIRESET.

Sam2 Sar

## Overview

Sam2 Sar is a dataset for instance segmentation tasks - it contains Ship annotations for 232 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).

Sam2 Food Dataset

## Overview

Sam2 Food Dataset is a dataset for object detection tasks - it contains Food annotations for 1,017 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).

stark2000s/segment-images-sam2 dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by liukunshan2

Released under MIT

Contents

Sam2.1

## Overview

Sam2.1 is a dataset for instance segmentation tasks - it contains Segmenatation_scratch annotations for 276 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).

These figures are the graphical results of my Master 2 internship on automatic segmentation using SAM2(Segment Anything Model 2)an artificial intelligence. The red line represents the best cell line from which anatomical measurements were made.

Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes.

   This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for USGS Monitoring Site East Branch Brandywine Creek below Downingtown, PA. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/PA_East_Branch_Brandywine_Creek_below_Downingtown for this specific data set) and/or the National Imagery Management System (NIMS) API.

   Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json).

   All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated during this process.

   This project was conducted in 2023-2025 by collaborators at the University of Nebraska-Lincoln, University of Nebraska at Kearney, and the U.S. Geological Survey.

   This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G23AC00141-00. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. We gratefully acknowledge graduate student support from Daugherty Water for Food Global Institute at the University of Nebraska.

Traffic Signs Sam2

## Overview

Traffic Signs Sam2 is a dataset for instance segmentation tasks - it contains Traffic Signs annotations for 2,031 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).

Dataset

This dataset was created by Pawankumar1246

Contents

hf-internal-testing/sam2-fixtures dataset hosted on Hugging Face and contributed by the HF Datasets community