Model Personalization on Edge
Improving the performance of DL models for individual users by re-training on a user's data on the edge.
QMNIST Dataset
Divide the datset by the writer ID.
python preprocess/filter_by_user.py --download --dataset=train
python preprocess/filter_by_user.py --download --dataset=test
The resulting user-specific datasets will be under data/QMNIST/train and data/QMNIST/test.
The file naming convention is w-<witer_id>.pth.
To load the dataset of a specific writer:
import torch
from torch.utils.data import DataLoader
dataset = torch.load('data/QMNIST/train/w-<writer_id>.pth')
dataloader = DataLoader(dataset)
License
BSD 3-Clause License.
1 maintainer
Metadata
1 maintainer
Release files for edgify 0.0.1
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