阳江市阳春市文化站信息|文化站信息数据集|公共数据数据集
收藏Market-1501
1501市场的数据集是在清华大学的一家超市前收集的。总共使用了六个摄像头,其中包括5个高分辨率摄像头和一个低分辨率摄像头。不同摄像机之间存在视场重叠。总体而言,该数据集包含32,668带注释的1,501身份的边界框。在这个开放系统中,每个身份的图像最多由六个摄像机捕获。我们确保每个带注释的身份都存在于至少两个摄像机中,以便可以执行跨摄像机搜索。1501市场的数据集有三个特色属性: 首先,我们的数据集使用可变形零件模型 (DPM) 作为行人检测器。 其次,除了真正界框外,我们还提供了误报检测结果。 第三,每个标识在每个摄像机下可能具有多个图像。在跨摄像头搜索期间,每个身份都有多个查询和多个地面真相。
OpenDataLab 收录
TinyPerson
TinyPerson是远距离且具有大量背景的微小物体检测的基准。TinyPerson中的图像是从互联网上收集的。首先,从不同的网站收集高分辨率的视频。其次,每50帧对视频中的图像进行采样。然后删除具有一定重复 (同质性) 的图像,并且用手用边界框用72,651对象注释所得图像。
OpenDataLab 收录
青岛市财政局公文法规信息
市财政局公文法规信息
山东公共数据开放网 收录
bsgreenb/cats_vs_dogs
该数据集包含图像、标签和ID三个特征。标签是分类标签,0代表猫,1代表狗。数据集分为训练集和测试集,训练集包含25000个样本,测试集包含12500个样本。数据集的下载大小为859839390字节,数据集大小为852010512.5字节。
hugging_face 收录
didsr/tsynth
--- license: cc0-1.0 task_categories: - image-classification - image-segmentation tags: - medical pretty_name: T-SYNTH size_categories: - 1K<n<10K --- # T-SYNTH <!-- Provide a quick summary of the dataset. --> T-SYNTH is a synthetic digital breast tomosynthesis (DBT) dataset with four breast fibroglandular density distributions imaged using Monte Carlo x-ray simulations with the publicly available [Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE)](https://github.com/DIDSR/VICTRE) toolkit. ## Dataset Details The dataset has the following characteristics: * Breast density: dense, heterogeneously dense, scattered, fatty * Mass radius (mm): 5.00, 7.00, 9.00 * Mass density: 1.0, 1.06, 1.1 (ratio of mass radiodensity to that of fibroglandular tissue) ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [Christopher Wiedeman](https://www.linkedin.com/in/christopher-wiedeman-a0b01014b), [Anastasiia Sarmakeeva](https://www.linkedin.com/in/anastasiia-sarmakeeva/), [Elena Sizikova](https://esizikova.github.io/), [Daniil Filienko](https://www.linkedin.com/in/daniil-filienko-800160215/), [Miguel Lago](https://www.linkedin.com/in/milaan/), [Jana Gut Delfino](https://www.linkedin.com/in/janadelfino/), [Aldo Badano](https://www.linkedin.com/in/aldobadano/) - **License:** Creative Commons 1.0 Universal License (CC0) ## Data Acquisition Details **Imaging Modality:** Paired 2D digital mammography (DM) and 3D digital breast tomosynthesis (DBT) images. The DBT images are projected into C-VIEW via the method of (Klein, 2023). **Manufacturer and Model:** Replica of the Siemens detector based on MC-GPU (Badal and Badano, 2009). **Demographics:** All breast phantoms are synthetic and do not represent real human subjects. **Cohort Description:** 9,000 synthetic digital breast tomosynthesis (DBT) samples, distributed across four breast density categories: | Breast Density | Fatty | Scattered | Hetero | Dense | **Total** | | --------- | --------- | --------- | ------- | ------- | --------- | | **Les.-free / Les.-present** | 1350/1350 | 1350/1350 | 900/900 | 900/900 | 4500/4500 | **Annotation Protocols:** Lesion masks and bounding boxes were generated automatically from the phantom. **Data Format and Structure:** Image files are in .raw format. ### Dataset Sources <!-- Provide the basic links for the dataset. --> - **Code:** [https://github.com/DIDSR/tsynth-release](https://github.com/DIDSR/tsynth-release) ## Intended Use <!-- Address questions around how the dataset is intended to be used. --> T-SYNTH is intended to facilitate testing of AI with pre-computed synthetic digital breast tomosynthesis (DBT) data, complementing the M-SYNTH synthetic mammography dataset. ## Ethical Considerations This work is using synthetically generated data and does not include any real patient-identifiable information. Publication of synthetic data aims to promote transparency, reproducibility, and fairness in medical AI research. We recommend avoiding training models only on synthetic data without appropriate validation. ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> Directory layout: ``` T-SYNTH/data/ ├── cview ├── embed_metadata ├── pretrained_models ├── results └── volumes_subset ``` Descriptions: * **`cview/`** -- contains T-SYNTH C-VIEW images. * **`embed_metadata/`** -- Configuration files needed to reproduce experiments. * **`pretrained_models/`** -- Pretrained models for ```DBT```, ```DM``` and ```diffusion``` experiments to reproduce results from the paper. Note to reproduce you need files from **`embed_metadata/`**. * **`results/`** -- Output data and plots used in the paper (see [T-SYNTH repository](https://github.com/DIDSR/tsynth-release/tree/main/code/notebooks)). Description for each experiment could be found [here](https://github.com/DIDSR/tsynth-release/blob/main/code/README.md#experiment-configuration-map). * **`volumes_subset/`** -- example of volumetric data. The full data set will be released later due to volume. The data is organized by lesion size, breast density and lesion density. Folder names follow the convention: ```output_cview_det_Victre/device_data_VICTREPhantoms_spic_[LESION_DENSITY]/[BREAST_DENSITY]/2/[LESION_SIZE]/SIM.zip```. Example path in `volumes_subset`: ``` device_data_VICTREPhantoms_spic_1.1/fatty/2/5.0/SIM/D2_5.0_fatty.1/1/ ├── reconstruction1.loc # Lesion coordinates ├── reconstruction1.mhd # Metadata (raw format) ├── reconstruction1.raw # Raw image data └── reconstruction1_mask.h5 # Pixel-level segmentation masks for lesions and tissues ``` ## How to use it The description how to use it could be found [here](https://github.com/DIDSR/tsynth-release/blob/main/code/README.md). ## Citation ``` @article{t-synth, title={T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images}, author={Christopher Wiedeman, Anastasiia Sarmakeeva, Elena Sizikova, Daniil Filienko, Miguel Lago, Jana G. Delfino, Aldo Badano}, journal={}, volume={}, pages={}, year={2025} } ``` ## Related Links 1. [Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE)](https://cdrh-rst.fda.gov/victre-silico-breast-imaging-pipeline). 2. [M-SYNTH: A Dataset for the Comparative Evaluation of Mammography AI](https://cdrh-rst.fda.gov/m-synth-dataset-comparative-evaluation-mammography-ai). 6. A. Kim*, N. Saharkhiz*, E. Sizikova*, M. Lago, B. Sahiner, J. G. Delfino, A. Badano. [S-SYNTH: Knowledge-Based, Synthetic Generation of Skin Images](https://github.com/DIDSR/ssynth-release). MICCAI 2024. 4. [FDA Catalog of Regulatory Science Tools to Help Assess New Medical Devices](https://www.fda.gov/medical-devices/science-and-research-medical-devices/catalog-regulatory-science-tools-help-assess-new-medical-devices).
hugging_face 收录