Omkar2118/datasets huggingface dataset:
Amazon Men
Full name: Amazon Men

This datasets is a subset of the Amazon reviews dataset which contain Men related products

Introducing paper: VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
Amazon Fashion
Full name: Amazon Fashion

This datasets is a subset of the Amazon reviews dataset which contain Fashion related products

Introducing paper: Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach
TCR
Full name: Temporal and Causal Reasoning dataset

A dataset of Joint Reasoning for Temporal and Causal Relations

Introducing paper: Joint Reasoning for Temporal and Causal Relations
RainCityscapes
Full name: RainCityscapes

A  dataset for rain removal with scene depth information.  
Compared with previous datasets, this dataset are all outdoor photos, each with a depth map, and the rain images exhibit different degrees of rain and fog.

Introducing paper: Depth-Attentional Features for Single-Image Rain Removal
PhC-C2DH-U373

Glioblastoma-astrocytoma U373 cells on a polyacrylamide substrate

Dr. S. Kumar. Department of Bioengineering, University of California at Berkeley, Berkeley CA (USA)
DIC-C2DH-HeLa

HeLa cells on a flat glass
Dr. G. van Cappellen. Erasmus Medical Center, Rotterdam, The Netherlands
Fluo-N2DH-SIM+

Simulated nuclei of HL60 cells stained with Hoescht

Dr. V. Ulman and Dr. D. Svoboda. Centre for Biomedical Image Analysis (CBIA),
Masaryk University, Brno, Czech Republic (Created using MitoGen, part of Cytopacq)
Fluo-N2DH-GOWT1

GFP-GOWT1 mouse stem cells

Dr. E. Bártová. Institute of Biophysics, Academy of Sciences of the Czech Republic, Brno, Czech Republic
Fluo-N2DL-HeLa

HeLa cells stably expressing H2b-GFP

Mitocheck Consortium
Fluo-N3DL-TRIC

Developing Tribolium Castaneum embryo (3D cartographic projection)

Dr. A. Jain. Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
Fluo-C3DH-A549-SIM

Simulated GFP-actin-stained A549 Lung Cancer cells embedded in a Matrigel matrix

Dr. M. Maška and Dr. D. V. Sorokin. Centre for Biomedical Image Analysis (CBIA),

Masaryk University, Brno, Czech Republic (Created using FiloGen, part of Cytopacq)
Fluo-C3DL-MDA231

MDA231 human breast carcinoma cells infected with a pMSCV vector including the GFP sequence, embedded in a collagen matrix

Dr. R. Kamm. Dept. of Biological Engineering, Massachusetts Institute of Technology, Cambridge MA (USA)
VISUELLE2.0
Full name: VISUELLE2.0

Visuelle 2.0 is a dataset containing real data for 5355 clothing products of the retail fast-fashion Italian company, Nuna Lie. Specifically, Visuelle 2.0 provides data from 6 fashion seasons (partitioned in Autumn-Winter and Spring-Summer) from 2017-2019, right before the Covid-19 pandemic. Each product is accompanied by an HD image, textual tags and more. The time series data are disaggregated at the shop level, and include the sales, inventory stock, max-normalized prices (for the sake of confidentiality} and discounts. Exogenous time series data is also provided, in the form of Google Trends based on the textual tags and multivariate weather conditions of the stores’ locations. Finally, we also provide purchase data for 667K customers whose identity has been anonymized, to capture personal preferences. With these data, Visuelle 2.0 allows to cope with several problems which characterize the activity of a fast fashion company: new product demand forecasting, short-observation new product sales forecasting, and product recommendation.

Introducing paper: The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark
CB-ToF
Full name: Cornell-Box Time-of-Flight Dataset

# Cornell-Box Dataset

## Download

The CornellBox Dataset can be downloaded from this URL

>https://viscom.datasets.uni-ulm.de/radu/dataset.zip


## Dataset

The dataset contains correlation measurements, ToF depth images and ground truth depth images in `.hdr` format.

The script `simulate_noise_on_correlations.py` can be used to simulate shot noise on the correlation images using the default arguments.

## Citing this work

If you use this data in your work, please kindly cite the following paper:

```
@InProceedings{schelling2022radu,
author = {Schelling, Michael and Hermosilla, Pedro and Ropinski, Timo},
title = {{RADU} - Ray-Aligned Depth Update Convolutions for {ToF} Data Denoising},
booktitle = {Conference on Computer Vision and Patter Recognition (CVPR)},
year = {2022}
}
```

## References

The data was generated using the transient renderer of Jarabo et al. [1].

[1] Jarabo, A., Marco, J., Mu&#x00F1;oz, A., Buisan, R., Jarosz, W., Gutierrez, D.: "A framework for transient rendering" ACM Transactions on Graphics, SIGGRAPH ASIA, (2014).

Introducing paper: RADU: Ray-Aligned Depth Update Convolutions for ToF Data Denoising
FLAT

FLAT, a synthetic dataset of 2000 ToF measurements that capture all of these nonidealities, and can be used to simulate different hardware

Introducing paper: Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset
DeePore
Full name: Deep learning for rapid characterization of porous materials

DeePore is a deep learning workflow for rapid estimation of a wide range of porous material properties based on the binarized micro–tomography images. By combining naturally occurring porous textures we generated 17,700 semi–real 3–D micro–structures of porous geo–materials with the size of $256^3$ voxels and 30 physical properties of each sample are calculated using physical simulations on the corresponding pore network models. 

In the related paper, a designed feed–forward convolutional neural network (CNN) is trained based on the dataset to estimate several morphological, hydraulic, electrical, and mechanical characteristics of the porous material in a fraction of a second. In order to fine–tune the CNN design, we tested 9 different training scenarios and selected the one with the highest average coefficient of determination (R2) equal to 0.885 for 1418 testing samples. Additionally, 3 independent synthetic images as well as 3 realistic tomography images have been tested using the proposed method and results are compared with pore network modelling and experimental data, respectively. Tested absolute permeabilities had around 13% relative error compared to the experimental data which is noticeable considering the accuracy of the direct numerical simulation methods such as Lattice Boltzmann and Finite Volume. The workflow is compatible with any physical size of the images due to its dimensionless approach and can be used to characterize large–scale 3–D images by averaging the model outputs for a sliding window that scans the whole geometry.

Introducing paper: DeePore: a deep learning workflow for rapid and comprehensive characterization of porous materials
Distinctions-646

Dinstinctions-646 are composed of 646 foreground images with manually annotated alpha mattes

Introducing paper: Attention-Guided Hierarchical Structure Aggregation for Image Matting