Datasets:

Yeongkwon
/

MNIST-M-flatten

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MNIST-M (tabular)

A flattened version of Mike0307/MNIST-M for tabular learning (XGBoost, AutoGluon, etc.).

Flatten procedure

Each row's image (PNG bytes) is decoded with PIL and converted to RGB.
The 32x32x3 pixels are flattened in row-major (H, W, C) order into 3072 features.
Columns: px_0 ... px_3071 (uint8, 0-255) plus label (int).
The original train/test split is preserved: train.csv, test.csv.

Restoring an image

import pandas as pd, numpy as np
from PIL import Image

row = pd.read_csv("train.csv", nrows=1).iloc[0]
arr = row.drop("label").to_numpy(np.uint8).reshape(32, 32, 3)
Image.fromarray(arr)

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