Update README.md

README.md

@@ -9,3 +9,39 @@ A curation of datasets for educations purposes.
 Since SciKit Learn's [California Housing dataset](https://scikit-learn.org/stable/datasets/real_world.html#california-housing-dataset) often fails to download this is the Data Frame of the data in CSV format.  
 By default [SciKit Learn use some pre processing](https://github.com/scikit-learn/scikit-learn/blob/d3898d9d57aeb1e960d266613a2e31b07bca39d7/sklearn/datasets/_california_housing.py#L208-L220) of the [original data](https://web.archive.org/web/20250912205745/https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html).  
 This CSV is the data after the processing of SciKit Learn.
+
+### CIFAR 10
+
+Since using SciKit Learn's `fetch_openml()` fails to download the `CIFAR_10` dataset this is an alternative.  
+It was generated by:
+
+```python
+dsTrain = torchvision.datasets.CIFAR10(root = dataFolderPath, train = True,  download = True)
+dsVal   = torchvision.datasets.CIFAR10(root = dataFolderPath, train = False, download = True)
+
+numSamples = len(dsTrain)
+tXTrain = np.zeros((numSamples, 32, 32, 3), dtype = np.uint8)
+vYTrain = np.zeros((numSamples,), dtype = np.uint8)
+
+for ii in range(numSamples):
+    tXi, valY = dsTrain[ii]
+    tXTrain[ii] = tXi
+    vYTrain[ii] = valY
+
+numSamples = len(dsVal)
+tXVal = np.zeros((numSamples, 32, 32, 3), dtype = np.uint8)
+vYVal = np.zeros((numSamples,), dtype = np.uint8)
+
+for ii in range(numSamples):
+    tXi, valY = dsVal[ii]
+    tXVal[ii] = tXi
+    vYVal[ii] = valY
+
+tX = np.concatenate((tXTrain, tXVal), axis = 0)
+vY = np.concatenate((vYTrain, vYVal), axis = 0)
+
+mX = np.reshape(tX, (tX.shape[0], -1))
+
+dfData = pd.DataFrame(np.concatenate((mX, vY[:, np.newaxis]), axis = 1), columns = [f'Pixel_{ii:04d}' for ii in range(mX.shape[1])] + ['Label'])
+dfData.to_csv('CIFAR10.csv', index = False)
+```