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Dataset description

OpenR1-Math-220k is a large-scale dataset for mathematical reasoning. It consists of 220k math problems with two to four reasoning traces generated by DeepSeek R1 for problems from NuminaMath 1.5. The traces were verified using Math Verify for most samples and Llama-3.3-70B-Instruct as a judge for 12% of the samples, and each problem contains at least one reasoning trace with a correct answer.

The dataset consists of two splits:

default with 94k problems and that achieves the best performance after SFT.
extended with 131k samples where we add data sources like cn_k12. This provides more reasoning traces, but we found that the performance after SFT to be lower than the default subset, likely because the questions from cn_k12 are less difficult than other sources.

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https://t.me/datasets1

Dataset Description:
This dataset includes two folders of images of people. One folder contains images of men, and the other contains images of women. The images include faces, upper bodies, and full bodies. This dataset can be used for various projects like gender recognition, human identification, and image classification.

Use Cases:
Gender Recognition: For algorithms that recognize gender based on images.
Human Identification: To improve models for identifying humans in images and videos.
Image Classification: For classifying images into categories of men and women.

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The data collected for this project were from the following sources:

Fabric Defect Dataset from Kaggle (Ranathunga, 2020)
Fabric Stain Dataset from Kaggle (Pathirana, 2020)
Aitex Fabric Image Database (Silvestre-Blanes et al., 2019)
Dataset from the Author of the Literature Review Paper (Peng, 2020): Only the non-defect images from the dataset were used.

About Dataset
Dataset Information

Image Size (GB): 3.2

Modalities: MR (with some PET/CT)

Number of Images: 22,036

Number of Participants: 26

Number of Series: 182

Number of Studies: 26

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https://t.me/datasets1

I created this dataset because I found that many Alzheimer's MRI datasets on Kaggle are highly repetitive (all based on the 6400-image version, with various augmented datasets), and they lack specific data sources. This causes issues for research and citation. This dataset is sourced from OASIS and includes MRI images (axial slices) of 457 individuals (note that there is a data imbalance issue, please perform upsampling as needed). Each image is specifically named to help you locate the corresponding OASIS research phase and individual. I first extracted MRI images from 457*4 NIfTI files (each person has three MRI scan NIfTI files) and converted them to PNG format. Then, I performed skull stripping on the converted MRIs. Finally, I manually removed images with black regions and incomplete brain displays, which took a lot of time. If this dataset is well-received, I will consider releasing a skull-stripped dataset from ADNI. I hope Kagglers can use it to improve the accuracy of Alzheimer's diagnosis using various deep learning frameworks and contribute to Alzheimer's research.

2024-12-1 There are four nii files in the ‘VeryMildDemented’ folder that I forgot to delete. However, this does not affect the images imported using tools like ImageFolder. If you batch convert the images to three channels, it may cause errors. Please search for ‘brain.nii’ and ‘mask.nii’ in the folder and delete them manually.