This is the dataset used for pre-training in "ReasonBERT: Pre-trained to Reason with Distant Supervision", EMNLP'21.

There are two files:

sentence_pairs_for_pretrain_no_tokenization.tar.gz -> Contain only sentences as evidence, Text-only

table_pairs_for_pretrain_no_tokenization.tar.gz -> At least one piece of evidence is a table, Hybrid

The data is chunked into multiple tar files for easy loading. We use WebDataset, a PyTorch Dataset (IterableDataset) implementation providing efficient sequential/streaming data access.

For pre-training code, or if you have any questions, please check our GitHub repo https://github.com/sunlab-osu/ReasonBERT

Below is a sample code snippet to load the data

import webdataset as wds

path to the uncompressed files, should be a directory with a set of tar files

url = './sentence_multi_pairs_for_pretrain_no_tokenization/{000000...000763}.tar' dataset = ( wds.Dataset(url) .shuffle(1000) # cache 1000 samples and shuffle .decode() .to_tuple("json") .batched(20) # group every 20 examples into a batch )

Please see the documentation for WebDataset for more details about how to use it as dataloader for Pytorch

You can also iterate through all examples and dump them with your preferred data format

Below we show how the data is organized with two examples.

Text-only

{'s1_text': 'Sils is a municipality in the comarca of Selva, in Catalonia, Spain.', # query sentence 's1_all_links': { 'Sils,_Girona': [[0, 4]], 'municipality': [[10, 22]], 'Comarques_of_Catalonia': [[30, 37]], 'Selva': [[41, 46]], 'Catalonia': [[51, 60]] }, # list of entities and their mentions in the sentence (start, end location) 'pairs': [ # other sentences that share common entity pair with the query, group by shared entity pairs { 'pair': ['Comarques_of_Catalonia', 'Selva'], # the common entity pair 's1_pair_locs': [[[30, 37]], [[41, 46]]], # mention of the entity pair in the query 's2s': [ # list of other sentences that contain the common entity pair, or evidence { 'md5': '2777e32bddd6ec414f0bc7a0b7fea331', 'text': 'Selva is a coastal comarque (county) in Catalonia, Spain, located between the mountain range known as the Serralada Transversal or Puigsacalm and the Costa Brava (part of the Mediterranean coast). Unusually, it is divided between the provinces of Girona and Barcelona, with Fogars de la Selva being part of Barcelona province and all other municipalities falling inside Girona province. Also unusually, its capital, Santa Coloma de Farners, is no longer among its larger municipalities, with the coastal towns of Blanes and Lloret de Mar having far surpassed it in size.', 's_loc': [0, 27], # in addition to the sentence containing the common entity pair, we also keep its surrounding context. 's_loc' is the start/end location of the actual evidence sentence 'pair_locs': [ # mentions of the entity pair in the evidence [[19, 27]], # mentions of entity 1 [[0, 5], [288, 293]] # mentions of entity 2 ], 'all_links': { 'Selva': [[0, 5], [288, 293]], 'Comarques_of_Catalonia': [[19, 27]], 'Catalonia': [[40, 49]] } } ,...] # there are multiple evidence sentences }, ,...] # there are multiple entity pairs in the query }

Hybrid

{'s1_text': 'The 2006 Major League Baseball All-Star Game was the 77th playing of the midseason exhibition baseball game between the all-stars of the American League (AL) and National League (NL), the two leagues comprising Major League Baseball.', 's1_all_links': {...}, # same as text-only 'sentence_pairs': [{'pair': ..., 's1_pair_locs': ..., 's2s': [...]}], # same as text-only 'table_pairs': [ 'tid': 'Major_League_Baseball-1', 'text':[ ['World Series Records', 'World Series Records', ...], ['Team', 'Number of Series won', ...], ['St. Louis Cardinals (NL)', '11', ...], ...] # table content, list of rows 'index':[ [[0, 0], [0, 1], ...], [[1, 0], [1, 1], ...], ...] # index of each cell [row_id, col_id]. we keep only a table snippet, but the index here is from the original table. 'value_ranks':[ [0, 0, ...], [0, 0, ...], [0, 10, ...], ...] # if the cell contain numeric value/date, this is its rank ordered from small to large, follow TAPAS 'value_inv_ranks': [], # inverse rank 'all_links':{ 'St._Louis_Cardinals': { '2': [ [[2, 0], [0, 19]], # [[row_id, col_id], [start, end]] ] # list of mentions in the second row, the key is row_id }, 'CARDINAL:11': {'2': [[[2, 1], [0, 2]]], '8': [[[8, 3], [0, 2]]]}, } 'name': '', # table name, if exists 'pairs': { 'pair': ['American_League', 'National_League'], 's1_pair_locs': [[[137, 152]], [[162, 177]]], # mention in the query 'table_pair_locs': { '17': [ # mention of entity pair in row 17 [ [[17, 0], [3, 18]], [[17, 1], [3, 18]], [[17, 2], [3, 18]], [[17, 3], [3, 18]] ], # mention of the first entity [ [[17, 0], [21, 36]], [[17, 1], [21, 36]], ] # mention of the second entity ] } } ] }

This dataset contains images in png format for RSNA Screening Mammography Breast Cancer Detection competition. All the images have a 768x768 size and are organized in two folders (one for each label). This allows inferring the labels from the folders (which might be helpful when using keras image_dataset_from_directory for example). - The processing of the DICOM files was done in this notebook by (Radek Osmulski ): https://www.kaggle.com/code/radek1/how-to-process-dicom-images-to-pngs?scriptVersionId=113529850 - The contribution made in this dataset is to organize the files in a way that enables label inferring from the directory name.

🎉If this dataset helps you in your work don't hesitate to upvote 🎉

On the Generalization of WiFi-based Person-centric Sensing in Through-Wall Scenarios

This repository contains the 3DO dataset proposed in [1].

PyTroch Dataloader

A minimal PyTorch dataloader for the 3DO dataset is provided at: https://github.com/StrohmayerJ/3DO

Dataset Description

The 3DO dataset comprises 42 five-minute recordings (~1.25M WiFi packets) of three human activities performed by a single person, captured in a WiFi through-wall sensing scenario over three consecutive days. Each WiFi packet is annotated with a 3D trajectory label and a class label for the activities: no person/background (0), walking (1), sitting (2), and lying (3). (Note: The labels returned in our dataloader example are walking (0), sitting (1), and lying (2), because background sequences are not used.)

The directories 3DO/d1/, 3DO/d2/, and 3DO/d3/ contain the sequences from days 1, 2, and 3, respectively. Furthermore, each sequence directory (e.g., 3DO/d1/w1/) contains a csiposreg.csv file storing the raw WiFi packet time series and a csiposreg_complex.npy cache file, which stores the complex Channel State Information (CSI) of the WiFi packet time series. (If missing, csiposreg_complex.npy is automatically generated by the provided dataloader.)

Dataset Structure:

/3DO

├── d1 <-- day 1 subdirectory

  └── w1 <-- sequence subdirectory

     └── csiposreg.csv <-- raw WiFi packet time series

     └── csiposreg_complex.npy <-- CSI time series cache

├── d2 <-- day 2 subdirectory

├── d3 <-- day 3 subdirectory

In [1], we use the following training, validation, and test split:

Subset Day Sequences

Train 1 w1, w2, w3, s1, s2, s3, l1, l2, l3

Val 1 w4, s4, l4

Test 1 w5 , s5, l5

Test 2 w1, w2, w3, w4, w5, s1, s2, s3, s4, s5, l1, l2, l3, l4, l5

Test 3 w1, w2, w4, w5, s1, s2, s3, s4, s5, l1, l2, l4

w = walking, s = sitting and l= lying

Note: On each day, we additionally recorded three ten-minute background sequences (b1, b2, b3), which are provided as well.

Download and UseThis data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].

[1] Strohmayer, J., Kampel, M. (2025). On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios. In: Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15315. Springer, Cham. https://doi.org/10.1007/978-3-031-78354-8_13

BibTeX citation:

@inproceedings{strohmayerOn2025, author="Strohmayer, Julian and Kampel, Martin", title="On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios", booktitle="Pattern Recognition", year="2025", publisher="Springer Nature Switzerland", address="Cham", pages="194--211", isbn="978-3-031-78354-8" }

Feral Cat Segmentation Dataset

Overview

This dataset provides image segmentation data for feral cats, designed for computer vision and machine learning tasks. It builds upon the original public domain dataset by Paul Cashman from Roboflow, with additional preprocessing and multiple data formats for easier consumption.

Dataset Source

• Original Author: Paul Cashman
• Original Source: Roboflow Universe
• Extended by: Lu Hou Yang
• GitHub: https://github.com/luhouyang/open_circles
• License: Public Domain

Dataset Contents

The dataset is organized into three standard splits: - Train set - Validation set - Test set

Each split contains data in multiple formats: 1. Original JPG images 2. Segmentation mask JPG images 3. Parquet files containing flattened image and mask data 4. Pickle files containing serialized image and mask data

Data Formats

1. Image Files

• Format: JPG
• Resolution: 224×224 pixels
• Directory Structure:
  • train/: Original training images
  • valid/: Original validation images
  • test/: Original test images
  • train_mask/: Corresponding segmentation masks for training
  • valid_mask/: Corresponding segmentation masks for validation
  • test_mask/: Corresponding segmentation masks for testing

2. Parquet Files

• Files: train_dataset.parquet, valid_dataset.parquet, test_dataset.parquet
• Content: Flattened image data and corresponding masks combined in a single table
• Structure: Each row contains the flattened pixel values of an image followed by the flattened pixel values of its mask
• Data Division: Image and mask data are split at index split_at = image_size[0] * image_size[1] * image_channels
  • Data before this index: image pixel values (reshaped to [-1, 224, 224, 3])
  • Data after this index: mask pixel values (reshaped to [-1, 224, 224, 1])
• Benefits: Efficient storage and faster loading compared to individual image files

3. Pickle Files

• Files: train_dataset.pkl, valid_dataset.pkl, test_dataset.pkl
• Content: Serialized Python objects containing images and their corresponding masks
• Structure: List of [image, mask] pairs, where each image and mask is serialized using Python's pickle
• Data Access: Similar to parquet files, when loaded through the provided dataset class, data is split at the same index: split_at = image_size[0] * image_size[1] * image_channels
• Benefits: Preserves original data structure and enables quick loading in Python

4. CSV Files

• Files: train_dataset.csv, valid_dataset.csv, test_dataset.csv
• Content: Same data as parquet files but in CSV format
• Structure: No headers, raw flattened pixel values
• Data Division: Same split point as parquet files

Image Preprocessing

All images were preprocessed with the following operations: - Resized to 224×224 pixels using bilinear interpolation - Segmentation masks were also resized to match the images using nearest neighbor interpolation - Original RLE (Run-Length Encoding) segmentation data converted to binary masks

Data Normalization

When used with the provided PyTorch dataset class, images are normalized with: - Mean: [0.48235, 0.45882, 0.40784] - Standard Deviation: [0.00392156862745098, 0.00392156862745098, 0.00392156862745098]

PyTorch Integration

A custom CatDataset class is included for easy integration with PyTorch:

from cat_dataset import CatDataset

# Load from parquet format
dataset = CatDataset(
  root="path/to/dataset",
  split="train", # Options: "train", "valid", "test"
  format="parquet", # Options: "parquet", "pkl"
  image_size=[224, 224],
  image_channels=3,
  mask_channels=1
)

# Use with PyTorch DataLoader
from torch.utils.data import DataLoader
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

Performance Comparison

Loading time benchmarks from the original implementation: - Parquet format: ~1.29 seconds per iteration - Pickle format: ~0.71 seconds per iteration

The pickle format provides the fastest loading times and is recommended for most use cases.

Citation

If you use this dataset in your research or projects, please cite:

@misc{feral-cat-segmentation_dataset,
 title = {feral-cat-segmentation Dataset},
 type = {Open Source Dataset},
 author = {Paul Cashman},
 howpublished = {\url{https://universe.roboflow.com/paul-cashman-mxgwb/feral-cat-segmentation}},
 url = {https://universe.roboflow.com/paul-cashman-mxgwb/feral-cat-segmentation},
 journal = {Roboflow Universe},
 publisher = {Roboflow},
 year = {2025},
 month = {mar},
 note = {visited on 2025-03-19},
}

Sample Usage Code

Basic Dataset Loading

from ca...

Pytorch Image Models (timm)

timm is a deep-learning library created by Ross Wightman and is a collection of SOTA computer vision models, layers, utilities, optimizers, schedulers, data-loaders, augmentations and also training/validating scripts with ability to reproduce ImageNet training results.

https://github.com/rwightman/pytorch-image-models

# Installation
!python -m pip install /kaggle/input/timm067py3/timm-0.6.7-py3-none-any.whl