This dataset is a filtered subset of the COCO 2017 dataset containing only the 'cat' class. The images and annotations are optimized for training object detection models

The IMPTOX project has received funding from the EU's H2020 framework programme for research and innovation under grant agreement n. 965173. Imptox is part of the European MNP cluster on human health.

More information about the project here.

Description: This repository includes the trained weights and a custom COCO-formatted dataset used for developing and testing a Faster R-CNN R_50_FPN_3x object detector, specifically designed to identify particles in micro-FTIR filter images.

Contents:

Weights File (neuralNetWeights_V3.pth):

Format: .pth

Description: This file contains the trained weights for a Faster R-CNN model with a ResNet-50 backbone and a Feature Pyramid Network (FPN), trained for 3x schedule. These weights are specifically tuned for detecting particles in micro-FTIR filter images.

Custom COCO Dataset (uFTIR_curated_square.v5-uftir_curated_square_2024-03-14.coco-segmentation.zip):

Format: .zip

Description: This zip archive contains a custom COCO-formatted dataset, including JPEG images and their corresponding annotation file. The dataset consists of images of micro-FTIR filters with annotated particles.

Contents:

Images: JPEG format images of micro-FTIR filters.

Annotations: A JSON file in COCO format providing detailed annotations of the particles in the images.

Management: The dataset can be managed and manipulated using the Pycocotools library, facilitating easy integration with existing COCO tools and workflows.

Applications: The provided weights and dataset are intended for researchers and practitioners in the field of microscopy and particle detection. The dataset and model can be used for further training, validation, and fine-tuning of object detection models in similar domains.

Usage Notes:

The neuralNetWeights_V3.pth file should be loaded into a PyTorch model compatible with the Faster R-CNN architecture, such as Detectron2.

The contents of uFTIR_curated_square.v5-uftir_curated_square_2024-03-14.coco-segmentation.zip should be extracted and can be used with any COCO-compatible object detection framework for training and evaluation purposes.

Code can be found on the related Github repository.

Min-Jaewon/sdxl-coco-filtered dataset hosted on Hugging Face and contributed by the HF Datasets community

shivam29981/filtered-coco dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Tugay Abdullazade

Contents

This Is Keypoint-Only subset from COCO 2017 Dataset. You can access the original COCO Dataset from here

This Dataset contains three folders: annotations, val2017, and train2017. - Contents in annotation folder is two jsons, for val dan train. Each jsons contains various informations, like the image id, bounding box, and keypoints locations. - Contents of val2017 and train2017 is various images that have been filtered. They are the images that have num_keypoints > 0 according to the annotation file.

numoverseg and nummiss of three BTS models. The best-performing results are highlighted using bold font. lr: learning rate.

Coco_filtered

## Overview

Coco_filtered is a dataset for object detection tasks - it contains Letters annotations for 5,731 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).

IoU and HD of UNets family when the optimizer is set as Adagrad. The best-performing results are highlighted using bold font.

This dataset is a curated subset of the "People Detection" dataset, filtered to include only images and annotations for the "Pedestrian" class. It is structured in COCO format, with separate folders for train, valid, and test splits.

Class: Pedestrian

Format: COCO JSON

Total Classes: 1

Annotation Type: Bounding Boxes

Source: Roboflow (original dataset)

Filtered and prepared for real-time pedestrian detection using YOLOv8

Ideal for training object detection models focused exclusively on pedestrian detection in surveillance, autonomous systems, or smart city applications.

IoU and HD of UNets family when the optimizer is set as Adam. The best-performing results are highlighted using bold font.

Our dataset consists of a collection of images and annotations of various transportation objects and people.

Specifically, we focus on six different classes: person, car, bicycle, motorcycle, bus, and train. The data was originally sourced from the well-known COCO dataset from 2017, which contains a much larger number of object classes. However, we filtered the labels using a Python code to select only the classes of interest for our dataset.

As a result, our dataset is tailored for use cases that specifically involve these six classes. Each image in the dataset is annotated with bounding boxes that correspond to the location of each object instance in the image.

The global carbon block filter market is booming, projected to reach $[estimated 2033 value] by 2033, driven by increasing water contamination concerns and demand for clean drinking water. This comprehensive analysis explores market size, growth trends, key players (Marmon, Multipure, etc.), and regional insights. Discover the opportunities and challenges in this expanding market.

This is the filtered version of DOTA v 1 data set . It contains the annotations for the following classes - "plane", "ship", "storage-tank", "harbor", "bridge", "large-vehicle", "small-vehicle", "helicopter". Also the annotation format is changed into COCO format to support axis based bounding.

Original dataset: https://captain-whu.github.io/DOTA/ Original authors: Xia et al. (2018) — DOTA: A Large-scale Dataset for Object Detection in Aerial Images. License: CC BY-NC-SA 4.0

@inproceedings{xia2018dota, title={DOTA: A Large-scale Dataset for Object Detection in Aerial Images}, author={Xia, Gui-Song and Bai, Xiang and Ding, Jian and Zhu, Zhen and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei}, booktitle={CVPR}, year={2018}, pages={3974--3983} }

Min-Jaewon/sdxl-coco-filtered-fp32 dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Navid Kanaani

Released under CC0: Public Domain

Contents

SpeechCoco

Introduction

Our corpus is an extension of the MS COCO image recognition and captioning dataset. MS COCO comprises images paired with a set of five captions. Yet, it does not include any speech. Therefore, we used Voxygen's text-to-speech system to synthesise the available captions.

The addition of speech as a new modality enables MSCOCO to be used for researches in the field of language acquisition, unsupervised term discovery, keyword spotting, or semantic embedding using speech and vision.

Our corpus is licensed under a Creative Commons Attribution 4.0 License.

Data Set

This corpus contains 616,767 spoken captions from MSCOCO's val2014 and train2014 subsets (respectively 414,113 for train2014 and 202,654 for val2014).

We used 8 different voices. 4 of them have a British accent (Paul, Bronwen, Judith, and Elizabeth) and the 4 others have an American accent (Phil, Bruce, Amanda, Jenny).

In order to make the captions sound more natural, we used SOX tempo command, enabling us to change the speed without changing the pitch. 1/3 of the captions are 10% slower than the original pace, 1/3 are 10% faster. The last third of the captions was kept untouched.

We also modified approximately 30% of the original captions and added disfluencies such as "um", "uh", "er" so that the captions would sound more natural.

Each WAV file is paired with a JSON file containing various information: timecode of each word in the caption, name of the speaker, name of the WAV file, etc. The JSON files have the following data structure:

{ "duration": float, "speaker": string, "synthesisedCaption": string, "timecode": list, "speed": float, "wavFilename": string, "captionID": int, "imgID": int, "disfluency": list }

On average, each caption comprises 10.79 tokens, disfluencies included. The WAV files are on average 3.52 seconds long.

Repository

The repository is organized as follows:

CORPUS-MSCOCO (~75GB once decompressed)

train2014/ : folder contains 413,915 captions

json/

wav/

translations/

train_en_ja.txt

train_translate.sqlite3

train_2014.sqlite3

val2014/ : folder contains 202,520 captions

json/

wav/

translations/

train_en_ja.txt

train_translate.sqlite3

val_2014.sqlite3

speechcoco_API/

speechcoco/

init.py

speechcoco.py

setup.py

Filenames

.wav files contain the spoken version of a caption

.json files contain all the metadata of a given WAV file

.sqlite3 files are SQLite databases containing all the information contained in the JSON files

We adopted the following naming convention for both the WAV and JSON files:

imageID_captionID_Speaker_DisfluencyPosition_Speed[.wav/.json]

Script

We created a script called speechcoco.py in order to handle the metadata and allow the user to easily find captions according to specific filters. The script uses the *.db files.

Features:

Aggregate all the information in the JSON files into a single SQLite database

Find captions according to specific filters (name, gender and nationality of the speaker, disfluency position, speed, duration, and words in the caption). The script automatically builds the SQLite query. The user can also provide his own SQLite query.

The following Python code returns all the captions spoken by a male with an American accent for which the speed was slowed down by 10% and that contain "keys" at any position

create SpeechCoco object

db = SpeechCoco(train_2014.sqlite3, train_translate.sqlite3, verbose=True)

filter captions (returns Caption Objects)

captions = db.filterCaptions(gender="Male", nationality="US", speed=0.9, text='%keys%') for caption in captions: print(' {}\t{}\t{}\t{}\t{}\t{}\t\t{}'.format(caption.imageID, caption.captionID, caption.speaker.name, caption.speaker.nationality, caption.speed, caption.filename, caption.text))

... 298817 26763 Phil 0.9 298817_26763_Phil_None_0-9.wav A group of turkeys with bushes in the background. 108505 147972 Phil 0.9 108505_147972_Phil_Middle_0-9.wav Person using a, um, slider cell phone with blue backlit keys. 258289 154380 Bruce 0.9 258289_154380_Bruce_None_0-9.wav Some donkeys and sheep are in their green pens . 545312 201303 Phil 0.9 545312_201303_Phil_None_0-9.wav A man walking next to a couple of donkeys. ...

Find all the captions belonging to a specific image

captions = db.getImgCaptions(298817) for caption in captions: print(' {}'.format(caption.text))

Birds wondering through grassy ground next to bushes. A flock of turkeys are making their way up a hill. Um, ah. Two wild turkeys in a field walking around. Four wild turkeys and some bushes trees and weeds. A group of turkeys with bushes in the background.

Parse the timecodes and have them structured

input:

... [1926.3068, "SYL", ""], [1926.3068, "SEPR", " "], [1926.3068, "WORD", "white"], [1926.3068, "PHO", "w"], [2050.7955, "PHO", "ai"], [2144.6591, "PHO", "t"], [2179.3182, "SYL", ""], [2179.3182, "SEPR", " "] ...

output:

print(caption.timecode.parse())

... { 'begin': 1926.3068, 'end': 2179.3182, 'syllable': [{'begin': 1926.3068, 'end': 2179.3182, 'phoneme': [{'begin': 1926.3068, 'end': 2050.7955, 'value': 'w'}, {'begin': 2050.7955, 'end': 2144.6591, 'value': 'ai'}, {'begin': 2144.6591, 'end': 2179.3182, 'value': 't'}], 'value': 'wait'}], 'value': 'white' }, ...

Convert the timecodes to Praat TextGrid files

caption.timecode.toTextgrid(outputDir, level=3)

Get the words, syllables and phonemes between n seconds/milliseconds

The following Python code returns all the words between 0.2 and 0.6 seconds for which at least 50% of the word's total length is within the specified interval

pprint(caption.getWords(0.20, 0.60, seconds=True, level=1, olapthr=50))

... 404537 827239 Bruce US 0.9 404537_827239_Bruce_None_0-9.wav Eyeglasses, a cellphone, some keys and other pocket items are all laid out on the cloth. . [ { 'begin': 0.0, 'end': 0.7202778, 'overlapPercentage': 55.53412863758955, 'word': 'eyeglasses' } ] ...

Get the translations of the selected captions

As for now, only japanese translations are available. We also used Kytea to tokenize and tag the captions translated with Google Translate

captions = db.getImgCaptions(298817) for caption in captions: print(' {}'.format(caption.text))

# Get translations and POS
print('\tja_google: {}'.format(db.getTranslation(caption.captionID, "ja_google")))
print('\t\tja_google_tokens: {}'.format(db.getTokens(caption.captionID, "ja_google")))
print('\t\tja_google_pos: {}'.format(db.getPOS(caption.captionID, "ja_google")))
print('\tja_excite: {}'.format(db.getTranslation(caption.captionID, "ja_excite")))

Birds wondering through grassy ground next to bushes. ja_google: 鳥は茂みの下に茂った地面を抱えています。 ja_google_tokens: 鳥 は 茂み の 下 に 茂 っ た 地面 を 抱え て い ま す 。 ja_google_pos: 鳥/名詞/とり は/助詞/は 茂み/名詞/しげみ の/助詞/の 下/名詞/した に/助詞/に 茂/動詞/しげ っ/語尾/っ た/助動詞/た 地面/名詞/じめん を/助詞/を 抱え/動詞/かかえ て/助詞/て い/動詞/い ま/助動詞/ま す/語尾/す 。/補助記号/。 ja_excite: 低木と隣接した草深いグラウンドを通って疑う鳥。

A flock of turkeys are making their way up a hill. ja_google: 七面鳥の群れが丘を上っています。 ja_google_tokens: 七 面 鳥 の 群れ が 丘 を 上 っ て い ま す 。 ja_google_pos: 七/名詞/なな 面/名詞/めん 鳥/名詞/とり の/助詞/の 群れ/名詞/むれ が/助詞/が 丘/名詞/おか を/助詞/を 上/動詞/のぼ っ/語尾/っ て/助詞/て い/動詞/い ま/助動詞/ま す/語尾/す 。/補助記号/。 ja_excite: 七面鳥の群れは丘の上で進んでいる。

Um, ah. Two wild turkeys in a field walking around. ja_google: 野生のシチメンチョウ、野生の七面鳥 ja_google_tokens: 野生 の シチメンチョウ 、 野生 の 七 面 鳥 ja_google_pos: 野生/名詞/やせい の/助詞/の シチメンチョウ/名詞/しちめんちょう 、/補助記号/、 野生/名詞/やせい の/助詞/の 七/名詞/なな 面/名詞/めん 鳥/名詞/ちょう ja_excite: まわりで移動しているフィールドの2羽の野生の七面鳥

Four wild turkeys and some bushes trees and weeds. ja_google: 4本の野生のシチメンチョウといくつかの茂みの木と雑草 ja_google_tokens: 4 本 の 野生 の シチメンチョウ と いく つ か の 茂み の 木 と 雑草 ja_google_pos: 4/名詞/4 本/接尾辞/ほん の/助詞/の 野生/名詞/やせい の/助詞/の シチメンチョウ/名詞/しちめんちょう と/助詞/と

DeepSeek v3.1 Quality-Filtered Referring Expression Parsing + Distractor Labels

This dataset contains parsed referring expressions from the RefCOCO, RefCOCOg, and RefCOCO+ validation sets, processed using DeepSeek v3.1 with quality filtering, plus corresponding distractor label annotations in COCO format.

  Dataset Description





  Overview

Model: DeepSeek v3.1 (deepseek-chat) Processing: Quality-filtered results from referring expression parsing Datasets: RefCOCO… See the full description on the dataset page: https://huggingface.co/datasets/dddraxxx/filtered_deepseek_v31_referring_expression_parsing.

This dataset is about books. It has 1 row and is filtered where the book is What Coco Chanel can teach you about fashion. It features 7 columns including author, publication date, language, and book publisher.

In 2022, brand awareness of Kellogg's Coco Pops was ** percent among survey respondents. When asked how many UK consumers ate Coco Pops in the past 12 months, just over a third of respondents stated that they had.

Want more brand data?Explore more Statista Brand Profiles. If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Statista Brand Profiler has you covered.