Information for how to cite the MTE bundle.

Target Corporation operates as a general merchandise retailer in the United States. The company offers food assortments, including perishables, dry grocery, dairy, and frozen items; apparel, accessories, home décor products, electronics, toys, seasonal offerings, food, and other merchandise; and beauty and household essentials. It also provides in-store amenities, such as Target Café, Target Optical, Starbucks, and other food service offerings. The company sells its products through its stores; and digital channels, including Target.com. As of March 09, 2022, the company operated approximately 2,000 stores. Target Corporation was incorporated in 1902 and is headquartered in Minneapolis, Minnesota.

Abbreviations: AE = adverse event; CI = confidence interval; CR = complete response; ECOG PS = Eastern Cooperative Oncology Group performance status; HFSR = hand-foot skin reaction; HR = hazard ratio; MSKCC = Memorial Sloan Kettering Cancer Center; NE = estimable; NR = not reported; OS = overall survival; PBO = placebo; PFS = progression-free survival; PR = partial response; SAE = serious adverse event; SD = stable disease.a Unless otherwise specified, refers to entire study population.b Data missing for 36 pts.c Data missing for 2 pts.d Data missing for 5 pts.e Eligibility criteria included ECOG PS 0–2 with waivers granted to selected pts with PS 3 or 4.f n = 1891.g Considered treatment related.h All reported AEs were considered treatment related.TARGET and expanded access trials.

The total equity of Target with headquarters in the United States amounted to ***** billion U.S. dollars in 2024. The reported fiscal year ends on February 01.Compared to the earliest depicted value from 2020 this is a total increase by approximately **** billion U.S. dollars. The trend from 2020 to 2024 shows, however, that this increase did not happen continuously.

The Rossi X-ray Timing Explorer (RXTE) Index table was created for the purpose of providing a concise and easily accessible tracking of RXTE observations, both those already completed and those still scheduled to be done. Each entry in this table corresponds to a specific proposal/target combination or complete observation', in contrast to the RXTE Master table in which each entry corresponds to a specific proposal/target/ObsID combination orobserving segment'. A complete observation can consist of many (in some cases dozens) observing segments. This is a service provided by NASA HEASARC .

SmartDrive Target

## Overview

SmartDrive Target is a dataset for object detection tasks - it contains Person annotations for 811 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 revenue of Target with headquarters in the United States amounted to ****** billion U.S. dollars in 2024. The reported fiscal year ends on February 01.Compared to the earliest depicted value from 2020 this is a total increase by approximately ***** billion U.S. dollars. The trend from 2020 to 2024 shows, however, that this increase did not happen continuously.

Assisted Driving Target Detection 2

## Overview

Assisted Driving Target Detection 2 is a dataset for object detection tasks - it contains  LZfm annotations for 2,433 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).

Target And Arrow Detection

## Overview

Target And Arrow Detection is a dataset for object detection tasks - it contains Target Arrow annotations for 657 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 [MIT license](https://creativecommons.org/licenses/MIT).

As of February 1, 2025, California was the state with the most Target stores in the United States, with *** stores. The company had a total of ***** stores open throughout the United States that year.

The NOAA-20 - formerly the Joint Polar Satellite System-1 (JPSS-1) - Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) dark target (DT) aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The VIIRS incarnation of the DT aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua missions' Moderate Imaging Spectroradiometer (MODIS) instruments. Two separate and distinct DT algorithms exist. One helps retrieve aerosol information over ocean (dark in visible and longer wavelengths), while the second aids retrievals over vegetated/dark-soiled land (dark in the visible wavelengths).This orbit-level product (Short-name: AERDT_L2_VIIRS_NOAA20_NRT) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor's scanning geometry and Earth's curvature. Viewed differently, this product's resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. Hence, the Level-2 Dark Target Aerosol Optical Thickness data product incorporates 64 (750 m) pixels over a 6-minute acquisition. This Version-2 set of products is the first collection of the Level-2 Dark Target Aerosol derived from the NOAA-20 VIIRS source. Hence, it bears outlining the differences between the products derived from NOAA-20 VIIRS as against the Suomi National Polar-orbiting Partnership (NOAA20) VIIRS.

The RIPARIAS target species list is a species checklist dataset published by the Research Institute for Nature and Forest (INBO). It contains (1) the target species of the LIFE RIPARIAS project (LIFE19 NAT/BE/000953), all of them invasive alien species (IAS) of the Regulation (EU) 1143/2014 (https://ec.europa.eu/environment/nature/invasivealien/) and (2) the alert list species that currently do not occur in the LIFE RIPARIAS project area, but have proven to have negative impacts on biodiversity and need to be rapidly removed should they be encountered. The alert list was drafted within the LIFE RIPARIAS project following an evidence-based methodology involving climate matching and risk assessment (Branquart et al. 2022). By publishing this list on GBIF it can be used for general reference, early warning systems, data extractions, baseline reporting, project KPIs etc. Issues with the dataset can be reported at: https://github.com/riparias/riparias-target-list We have released this dataset to the public domain under a Creative Commons Zero waiver. We would appreciate it if you follow the INBO norms for data use (https://www.inbo.be/en/norms-data-use) when using the data. If you have any questions regarding this dataset, don't hesitate to contact us via the contact information provided in the metadata or via opendata@inbo.be. This dataset was published as open data for the LIFE RIPARIAS project (Reaching Integrated and Prompt Action in Response to Invasive Alien Species https://www.riparias.be/), with technical support provided by the Research Institute for Nature and Forest (INBO).

According to a survey conducted among private credit firms in the United States, *********** reported planning to invest in their domestic market in 2025. Additionally, ** percent reported plans to expand their investments to Canada, while ** percent also indicated the United Kingdom as a target location.

Target Corporation's annual revenue was $106.57 B in fiscal year 2025. The annual revenue decreased -$846.00 M from $107.41 B (in 2024) to $106.57 B (in 2025), representing a -0.79% year-over-year decline.

This repository includes data, object detection models, and processing scripts necessary to evaluate the accuracy of the object detection models created for the underwater target detection software demonstration on the RivGen turbine project and to reproduce the performance metrics (precision, recall, mAP50, mAP50-95) presented in the report.

• Contents - The included data consist of "images" and "labels". Images and labels were curated from 2021 and 2024 smolt outmigration periods at the project site in Igiugig, AK. Images are monochrome 8-bit images of objects (smolt, debris, and other) passing through the field of view of the deployed cameras during various operational stages of the RivGen turbine. Labels are text files indicating the class and bounding polygon of each object in an image. The provided labels use the "YOLO" label format, described in detail here: https://docs.ultralytics.com/datasets/detect/. Each image has an associated label with the same time string in its file name (e.g., 2024_05_25_09_01_57.98.jpg and 2024_05_25_09_01_57.98.txt). Time strings have the format %yyyy_%mm_%dd_%HH_%MM_%SS.%3f.

The 2021 and 2024 test datasets are included. These data were used to evaluate the accuracy of models version 1 (V1), V2, and V3, as described in the report. These data are organized into test directories found in the 2021 and 2024 directories, e.g., /data/2021/test/.

The "train" and "val" directories are also present as the presence of these directories is necessary for the included script to run successfully, though the associated data is not included.

The model weights files are included for models V1, V2, and V3 in the models directory, e.g., /models/v1_model_train_2021_val_2021_test_2021.pt.

Finally, the python script "test_models.py" is included, which loads each model and tests it against the associated test dataset. The resulting accuracy metrics are saved to figures in a ./runs/detect/ directory, which will be created when the script is run on the user's machine. Instructions for installing and using the python script are included in the README.

• Requirements - All instructions assume the user is using a computer using Ubuntu Linux 20.04+ with Python3.8+. Operation on other operating systems may require some modification to these instructions. Models will run on your computer's CUDA-capable NVIDIA GPU if one is available. The README.md provides instruction for installing the requirements from the requirements.py file.

The included data was revised/updated to reflect the post-access report for the project. Resource titles and descriptions were updated to reflect the most up-to-date resources. The file names were updated by adding a "_Ver1" to the end of the outdated file and a "_Ver2" to the end of the revised file.

Target 销售 - 当前值，历史数据，预测，统计，图表和经济日历 - Sep 2025.Data for Target | 销售 including historical, tables and charts were last updated by Trading Economics this last September in 2025.

Target Finder

## Overview

Target Finder is a dataset for object detection tasks - it contains Target annotations for 1,292 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).

Target Corporation operates a chain of general merchandise stores, which offer a wide variety of general merchandise and food to their customers. The company has operated primarily in the United States since its inception. Target did have a number of stores in operation in the Canadian market, but these were all closed in 2015. Target Corporation had revenues amounting to approximately 106.57 billion U.S. dollars in 2024, making it one of the leading American retailers. The development of an American retail giant The company started out as the Dayton Dry Goods Company in 1902, later changing its name to the Dayton Company, but more commonly known as Dayton’s. The company was run under the Dayton-Hudson Corporation banner up until the year 2000, when it was renamed Target Corporation. The company spread across the United States and even entered the Canadian market for brief period. Target Corporation has an impressive number of stores in the United States. Today, Target is one of the most valuable retail brands in the world. Where does Target’s revenue come from? Target Corporation sells a wide range of goods, such as food, apparel, household essentials, and seasonal offerings, as well as many others. The company also sells products online, through target.com. Target’s sales share is quite evenly distributed, with several high revenue segments.

In 2024, Target Corporation's advertising costs worldwide amounted to *** billion U.S. dollars. The corresponding spending stood at *** billion dollars in the previous three years. Previously, Target's ad expenditure peaked at **** billion dollars in 2019.

MassiveFold data generated for CASP16, in collaboration with CAPRI . 8040 predictions were generated for the target = 1005 x 8 sets of parameters. - a description of the setup can be found in MassiveFold_CASP16_Abstract.pdf - README.txt describes the contents of the main MassiveFold.tar.gz file - the main MassiveFold.tar.gz file contains all the predictions, divided into 8 folders named after the conditions. It contains predictions as well as pickle files, sequence alignments, rankings and plots. The README.txt file describes the contents of this tar.gz. - theonly_pdbs_MassiveFold.tar.gzis the result of thegather_runs.pyscript, without the pickle files. It contains a list of all the pdb files and ranking files with scores. -gather_runs.pyallows to gather the runs, to use preferentially to the one included in the mainMassiveFold.tar.gzat the time of the prediction phase, because it has been updated -combined_scores.csvfile contains the CASP assessment for the target (from https://predictioncenter.org/) -CAPRI_assessment.csvfile contains the CAPRI assessment for the target (from https://www.capri-docking.org/assessment/) -CAPRI_scoring.pdb.gz` is a multi-pdbs containing all the MassiveFold predictions for the target. It was used for the CAPRI scoring phase. MassiveFold, 1.2.3 AlphaFold, 2.3.2