Here, we devised a novel isothermal technique on the basis of standard multiple cross-displacement amplification (MCDA), which is assisted with self-avoiding molecular recognition system (SAMRS) components and antarctic thermal-sensitive uracil-DNA-glycosylase enzyme (AUDG), termed AUDG–SAMRS–MCDA. To enable product detection on the dipsticks, we firstly developed an analysis strategy, which did not require the labelled primers or probes, and thus, the analysis system avoids the false-positive results arising from undesired hybridization (between two labelled primers, or the labelled probe and primer). The SAMRS components are incorporated into MCDA primers for improve the assay’s specificity, which can prevent the false-positive results yielding from off-target hybrids, undesired interactions between (hetero-dimer) or within (self-dimerization) primers. Two additional components (AUDG enzyme and dUTP) were added into the reaction mixtures, which were used for removing the false-positive results generating from carryover contamination, and thus, the genuine positives results were produced from the amplification of target templates. For the demonstration, the label-free AUDG–SAMRS–MCDA technique was successfully applied to detect Pseudomonas aeruginosa from pure culture and blood samples. As a proof-of-concept technique, the label-free AUDG–SAMRS–MCDA method can be reconfigured to detect different target sequences by redesigning the specific primers.

DavidNguyen/ShareGPT4V-Sam dataset hosted on Hugging Face and contributed by the HF Datasets community

Segmentation With SAM

## Overview

Segmentation With SAM is a dataset for semantic segmentation tasks - it contains URBAN SEG annotations for 148 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).

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.

Segmentation models perform a pixel-wise classification by classifying the pixels into different classes. The classified pixels correspond to different objects or regions in the image. These models have a wide variety of use cases across multiple domains. When used with satellite and aerial imagery, these models can help to identify features such as building footprints, roads, water bodies, crop fields, etc.Generally, every segmentation model needs to be trained from scratch using a dataset labeled with the objects of interest. This can be an arduous and time-consuming task. Meta's Segment Anything Model (SAM) is aimed at creating a foundational model that can be used to segment (as the name suggests) anything using zero-shot learning and generalize across domains without additional training. SAM is trained on the Segment Anything 1-Billion mask dataset (SA-1B) which comprises a diverse set of 11 million images and over 1 billion masks. This makes the model highly robust in identifying object boundaries and differentiating between various objects across domains, even though it might have never seen them before. Use this model to extract masks of various objects in any image.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS. Fine-tuning the modelThis model can be fine-tuned using SamLoRA architecture in ArcGIS. Follow the guide and refer to this sample notebook to fine-tune this model.Input8-bit, 3-band imagery.OutputFeature class containing masks of various objects in the image.Applicable geographiesThe model is expected to work globally.Model architectureThis model is based on the open-source Segment Anything Model (SAM) by Meta.Training dataThis model has been trained on the Segment Anything 1-Billion mask dataset (SA-1B) which comprises a diverse set of 11 million images and over 1 billion masks.Sample resultsHere are a few results from the model.

This dataset contains a daily snapshot of active exclusion records entered by the U.S. Federal government identifying those parties excluded from receiving Federal contracts, certain subcontracts, and certain types of Federal financial and non-financial assistance and benefits. The data was formerly contained in the Excluded Parties List System (EPLS). In July 2012, EPLS was incorporated into the System for Award Management (SAM). SAM is now the electronic, web-based system that keeps its user community aware of administrative and statutory exclusions across the entire government, and individuals barred from entering the United States. Users must read the exclusion record completely to understand how it impacts the excluded party. Note - Here is the link for the SAM Functional Data Dictionary - https://www.sam.gov/SAM/transcript/SAM_Functional_Data_Dictionary.pdf

phi0112358/sam dataset hosted on Hugging Face and contributed by the HF Datasets community

Sam Segmentation

## Overview

Sam Segmentation is a dataset for instance segmentation tasks - it contains Fish annotations for 238 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).

USF Sam Glider deployment in the North Atlantic Ocean (July 2021)

SAM‑TP Traversability Dataset

This repository contains pixel‑wise traversability masks paired with egocentric RGB images, prepared in a flat, filename‑aligned layout that is convenient for training SAM‑2 / SAM‑TP‑style segmentation models.

Folder layout

. ├─ images/ # RGB frames (.jpg/.png). Filenames are globally unique. ├─ annotations/ # Binary masks (.png/.jpg). Filenames match images 1‑to‑1. └─ manifest.csv # Provenance rows and any missing‑pair notes.

Each… See the full description on the dataset page: https://huggingface.co/datasets/jamiewjm/sam-tp.

birdsql/share-sam dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Neel Shah

Contents

SAM-3D-Body Data

This repository provides the annotations used in SAM 3D Body.

  Datasets

3DPW AI Challenger COCO EgoExo4D EgoHumans Harmony4D MPII SA1B

  Get Started

Please follow the instructions to download and preocess the annotations.

  License

The SAM 3D Body data is licensed under SAM License.

  Citing SAM 3D Body

If you use SAM 3D Body or the SAM 3D Body dataset in your research, please use the following BibTeX entry.… See the full description on the dataset page: https://huggingface.co/datasets/facebook/sam-3d-body-dataset.

Sam Rwagatare Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Dataset Card for "test-sam"

More Information needed

Link to the Sam Transit website that provides public transit for Sioux Falls, South Dakota.

This dataset provides information about the number of properties, residents, and average property values for Magic Sam Court cross streets in Biltmore Lake, NC.

USF Sam Glider deployment in the Southeast US Atlantic Bight (2019)

This dataset consists of 25,000 entries, each structured as a fictional interaction between a professional named "Sam" and a technology-related scenario. The data includes questions and corresponding answers that focus on how Sam, in various professional roles, leverages different technologies to address specific business and technical challenges.

Dataset Structure:

1. Content: Each entry contains a question-answer pair:

   • Question: This simulates how Sam applies two different technologies (keywords) to improve or solve a problem in a specific area (topic) within the context of a given job role. The question is generated using a template that combines the technologies, topic, and job title in a structured way.
   • Answer: This elaborates on how Sam implements solutions using the mentioned technologies. The answer outlines the benefits, outcomes, and strategic goals achieved through Sam's expertise.

2. Metadata:

   • Timestamp: Each entry is timestamped with a date starting from January 1, 2024, with consecutive entries spaced by one day.

Key Components:

• Keywords: Represents different technologies and fields, such as AI, blockchain, cybersecurity, data science, cloud computing, and more. These keywords are randomly selected and paired in each entry.
• Topics: The dataset covers various business and operational topics like customer experience, risk management, innovation, and market trends, focusing on how technologies can be applied to improve these areas.
• Job Titles: Sam’s role changes across the dataset, with titles like Data Scientist, Blockchain Developer, AI Researcher, and Product Manager. These roles provide different perspectives on how the technologies are applied.
• Answer Details: The answers are generated using predefined templates and include aspects such as the benefits of the solutions Sam implements (e.g., increased productivity, reduced costs), challenges addressed, strategies employed, and the overall goals achieved.

Format:

The dataset is stored in JSON format with each entry containing a "content" field (containing the question-answer pair) and a "meta" field (containing the timestamp).

This dataset is from the City of Boston's Street Address Management (SAM) system, containing Boston addresses. Updated nightly and shared publicly.