NEU CLS

## Overview

NEU CLS is a dataset for classification tasks - it contains In Pa Cr Pi Ro Sc annotations for 1,799 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 Cross-Lingual Sentiment (CLS) dataset comprises about 800.000 Amazon product reviews in the four languages English, German, French, and Japanese.

toilaluan/cls-binary dataset hosted on Hugging Face and contributed by the HF Datasets community

The NEU-CLS dataset is collected from real industrial production lines. It is publicly released by the Surface Inspection Laboratory of Northeastern University, constructed explicitly for strip surface defect classification. This dataset covers six common types of strip surface defects: Crazing, Scratch, Pitted Surface, Patch, Rolled-in Scale, and Inclusion. The image's defect morphologies are diverse, presenting high complexity and diversity, effectively simulating the challenges of defect detection in practical application scenarios. Each defect category contains 300 images, all of which are grayscale with a resolution of 200×200 pixels.

Keda Cls

## Overview

Keda Cls is a dataset for classification tasks - it contains Pan annotations for 3,861 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).

Plant Cls

## Overview

Plant Cls is a dataset for classification tasks - it contains Plant annotations for 545 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).

crapthings/ins-cls dataset hosted on Hugging Face and contributed by the HF Datasets community

Real Fake Cls

## Overview

Real Fake Cls is a dataset for classification tasks - it contains Real Fake annotations for 3,311 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).

GC CLS

## Overview

GC CLS is a dataset for classification tasks - it contains Star annotations for 287 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).

arnodjiang/ivy-fake-cls dataset hosted on Hugging Face and contributed by the HF Datasets community

The Cloud Lidar System (CLS) was flown aboard the ER-2 to conduct cloud radiation measurements during CEPEX. The instrument was designed to operate at high altitudes in order to obtain measurements above the highest clouds providing the true height of cloud boundaries and the density structure of less dense clouds. The height structure of cirrus, cloud top density and multiple cloud layers are also provided. The laser type was ND:YAG I,II. Principal Investigator was Dr. James Spinhirne.

Dataset

This dataset was created by QUÂN PHAN TIẾN

Contents

The Cross-Lingual Sentiment (CLS) dataset comprises about 800.000 Amazon product reviews in the four languages English, German, French, and Japanese.

For more information on the construction of the dataset see (Prettenhofer and Stein, 2010) or the enclosed readme files. If you have a question after reading the paper and the readme files, please contact Peter Prettenhofer.

We provide the dataset in two formats: 1) a processed format which corresponds to the preprocessing (tokenization, etc.) in (Prettenhofer and Stein, 2010); 2) an unprocessed format which contains the full text of the reviews (e.g., for machine translation or feature engineering).

The dataset was first used by (Prettenhofer and Stein, 2010). It consists of Amazon product reviews for three product categories---books, dvds and music---written in four different languages: English, German, French, and Japanese. The German, French, and Japanese reviews were crawled from Amazon in November, 2009. The English reviews were sampled from the Multi-Domain Sentiment Dataset (Blitzer et. al., 2007). For each language-category pair there exist three sets of training documents, test documents, and unlabeled documents. The training and test sets comprise 2.000 documents each, whereas the number of unlabeled documents varies from 9.000 - 170.000.

arnodjiang/fake-cls dataset hosted on Hugging Face and contributed by the HF Datasets community

The revenue of Cls Holdings with headquarters in the United Kingdom amounted to ***** million British pounds in 2023. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2019 this is a total increase by approximately **** million British pounds. The trend from 2019 to 2023 shows ,furthermore, that this increase happened continuously.

MedPointS-CLS

This is the medical point cloud classification dataset from MedPointS, where data is input point cloud, and label is the class label. Each point cloud has been normalized and sub-sampled to 2048 points. The correspondence between class names and labels is listed as follows (the label value plus 1 is the actual key of following map): coarse_label_to_organ = {1: 'adrenalgland', 2: 'aorta', 3: 'autochthon', 4: 'bladder', 5: 'brain', 6: 'breast'… See the full description on the dataset page: https://huggingface.co/datasets/wlsdzyzl/MedPointS-cls.

ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). In ImageNet, we aim to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, we hope ImageNet will offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy.

The test split contains 100K images but no labels because no labels have been publicly released. We provide support for the test split from 2012 with the minor patch released on October 10, 2019. In order to manually download this data, a user must perform the following operations:

1. Download the 2012 test split available here.
2. Download the October 10, 2019 patch. There is a Google Drive link to the patch provided on the same page.
3. Combine the two tar-balls, manually overwriting any images in the original archive with images from the patch. According to the instructions on image-net.org, this procedure overwrites just a few images.

The resulting tar-ball may then be processed by TFDS.

To assess the accuracy of a model on the ImageNet test split, one must run inference on all images in the split, export those results to a text file that must be uploaded to the ImageNet evaluation server. The maintainers of the ImageNet evaluation server permits a single user to submit up to 2 submissions per week in order to prevent overfitting.

To evaluate the accuracy on the test split, one must first create an account at image-net.org. This account must be approved by the site administrator. After the account is created, one can submit the results to the test server at https://image-net.org/challenges/LSVRC/eval_server.php The submission consists of several ASCII text files corresponding to multiple tasks. The task of interest is "Classification submission (top-5 cls error)". A sample of an exported text file looks like the following:

771 778 794 387 650
363 691 764 923 427
737 369 430 531 124
755 930 755 59 168

The export format is described in full in "readme.txt" within the 2013 development kit available here: https://image-net.org/data/ILSVRC/2013/ILSVRC2013_devkit.tgz Please see the section entitled "3.3 CLS-LOC submission format". Briefly, the format of the text file is 100,000 lines corresponding to each image in the test split. Each line of integers correspond to the rank-ordered, top 5 predictions for each test image. The integers are 1-indexed corresponding to the line number in the corresponding labels file. See labels.txt.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('imagenet2012', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/imagenet2012-5.1.0.png" alt="Visualization" width="500px">

The total assets of Cls Holdings Usa with headquarters in the United States amounted to ***** million U.S. dollars in 2023. The reported fiscal year ends on May 31.Compared to the earliest depicted value from 2019 this is a total decrease by approximately ***** million U.S. dollars. The trend from 2019 to 2023 shows, however, that this decrease did not happen continuously.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Can CLS's (CLS) Reach for the Stars?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Dsail Porini Cls Dataset

## Overview

Dsail Porini Cls Dataset is a dataset for classification tasks - it contains Animal Species annotations for 583 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).