Fruits-360 dataset

A dataset with 94110 images of 141 fruits, vegetables and nuts

https://news.1rj.ru/str/datasets1 ✈️

Fruits-360 dataset

A dataset with 94110 images of 141 fruits, vegetables and nuts

https://news.1rj.ru/str/datasets1 ✈️

Amazon Books Reviews 🛒

Goodreads-books reviews and denoscriptions of each book

https://news.1rj.ru/str/datasets1 ✅

Amazon Books Reviews 🛒

Goodreads-books reviews and denoscriptions of each book

https://news.1rj.ru/str/datasets1 ⭐️

US Accidents (2016 - 2023)

A Countrywide Traffic Accident Dataset (2016 - 2023)

About Dataset
Denoscription
This is a countrywide car accident dataset that covers 49 states of the USA. The accident data were collected from February 2016 to March 2023, using multiple APIs that provide streaming traffic incident (or event) data. These APIs broadcast traffic data captured by various entities, including the US and state departments of transportation, law enforcement agencies, traffic cameras, and traffic sensors within the road networks. The dataset currently contains approximately 7.7 million accident records

US Accidents (2016 - 2023)

A Countrywide Traffic Accident Dataset (2016 - 2023)

https://news.1rj.ru/str/datasets1 ✅

US Accidents (2016 - 2023)

A Countrywide Traffic Accident Dataset (2016 - 2023)

https://news.1rj.ru/str/datasets1 🇺🇸

Mashroom Image Classification

https://news.1rj.ru/str/datasets1 ⭐️

Mashroom Image Classification

https://news.1rj.ru/str/datasets1 ⭐️

Ocular Disease Recognition

Right and left eye fundus photographs of 5000 patients

Ocular Disease Intelligent Recognition (ODIR) is a structured ophthalmic database of 5,000 patients with age, color fundus photographs from left and right eyes and doctors' diagnostic keywords from doctors.

This dataset is meant to represent ‘‘real-life’’ set of patient information collected by Shanggong Medical Technology Co., Ltd. from different hospitals/medical centers in China. In these institutions, fundus images are captured by various cameras in the market, such as Canon, Zeiss and Kowa, resulting into varied image resolutions.
Annotations were labeled by trained human readers with quality control management. They classify patient into eight labels including:

Normal (N),
Diabetes (D),
Glaucoma (G),
Cataract (C),
Age related Macular Degeneration (A),
Hypertension (H),
Pathological Myopia (M),
Other diseases/abnormalities (O)

https://news.1rj.ru/str/datasets1 👩‍💻

Ocular Disease Recognition

https://news.1rj.ru/str/datasets1 ❤️

Diabetic Retinopathy Diagnosis Dataset

A Comprehensive Dataset for Medical Image Analysis

https://news.1rj.ru/str/datasets1 ☠️

Diabetic Retinopathy Diagnosis Dataset

A Comprehensive Dataset for Medical Image Analysis

https://news.1rj.ru/str/datasets1 ❤️

Captcha Dataset

Captcha images with solutions of 6 digit numbers

Dataset Structure:
Train : Contains 6,000 images used for training the model.
Test : Contains 2,000 images used for evaluating the model's performance during testing.
Validation : Contains 2,000 images used for validating the model during the training process.
DataFrame:

A CSV file is included with the following columns:
image_path: The relative path to each image file.
solution: The 6-digit number present in the captcha image, which is the label for training and evaluation.

https://news.1rj.ru/str/datasets1 🦾

Captcha Dataset

https://news.1rj.ru/str/datasets1 🦾

Please 👍 or ⭐️

Teeth Segmentation on dental X-ray images

The dataset consists of 598 images with a total of 15,318 polygons

About Dataset

Humans in the Loop is excited to publish a new open access dataset for Teeth segmentation on dental radiology scans. The segmentation is done manually by 12 Humans in the Loop trainees in the Democratic Republic of Congo as part of their trainings, using the Panoramic radiography database published by Lopez et al. The dataset consists of 598 images with a total of 15,318 polygons, where each tooth is segmented with a different class.

https://news.1rj.ru/str/datasets1 ✅

DeepGlobe Road Extraction Dataset

Road Extraction Dataset from DeepGlobe Challenge

Data

The training data for Road Challenge contains 6226 satellite imagery in RGB, size 1024x1024.
The imagery has 50cm pixel resolution, collected by DigitalGlobe's satellite.
The dataset contains 1243 validation and 1101 test images (but no masks).

Label

Each satellite image is paired with a mask image for road labels. The mask is a grayscale image, with white standing for road pixel, and black standing for background.
File names for satellite images and the corresponding mask image are id _sat.jpg and id _mask.png. id is a randomized integer.
Please note:
The values of the mask image may not be pure 0 and 255. When converting to labels, please binarize them at threshold 128.
The labels are not perfect due to the cost for annotating segmentation mask, specially in rural regions. In addition, we intentionally didn't annotate small roads within farmlands.

https://news.1rj.ru/str/datasets1 ⭐️