MLclf 0.2.3

The project Machine Learning CLassiFication (MLclf)

The project is to transform the mini-imagenet dataset which is initially created for the few-shot learning (other dataset will come soon...) to the format that fit the classification task.

The transformed dataset is divided into train, validation and test dataset, each dataset of which includes 100 classes.

How to use this package:

from MLclf import MLclf
import torch

# Download the original mini-imagenet data:
MLclf.miniimagenet_download(Download=True)
# Transform the original data into the format that fits the task for classification:
train_dataset, validation_dataset, test_dataset = MLclf.miniimagenet_clf_dataset(ratio_train=0.6, ratio_val=0.2, seed_value=None, shuffle=True, save_clf_data=True)
# The dataset can be transformed to dataloader via torch: 
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=128, shuffle=True, num_workers=0)