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Integration:  dvc git github
Reto Schiegg 328c8a09d2
Merge pull request #3 from retoschiegg/feature/remove-dvc-usr
2 years ago
96544081df
remove dvc user from config
2 years ago
4c262a2aed
first running version of meta pseudo labels
3 years ago
4294d54b7a
add training results/models of implementation
3 years ago
src
4294d54b7a
add training results/models of implementation
3 years ago
4c262a2aed
first running version of meta pseudo labels
3 years ago
4c262a2aed
first running version of meta pseudo labels
3 years ago
4c262a2aed
first running version of meta pseudo labels
3 years ago
4c262a2aed
first running version of meta pseudo labels
3 years ago
4c262a2aed
first running version of meta pseudo labels
3 years ago
4294d54b7a
add training results/models of implementation
3 years ago
4294d54b7a
add training results/models of implementation
3 years ago
4294d54b7a
add training results/models of implementation
3 years ago
4294d54b7a
add training results/models of implementation
3 years ago
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README.md

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Meta Pseudo Labels

Unofficial TF2 implementation of "Meta Pseudo Labels" (official Paper and Code).

Results

CIFAR-10-4K
w/o finetune w/ finetune
Paper 96.08%
Implementation 94.70% 94.71%

Usage

Setup Environment

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
pre-commit install

Run Scripts

export PYTHONPATH=src

# download dataset
dvc pull -r origin

# train & evaluate
python src/main.py --data-dir data/cifar10 --config-name cifar10 --model-dir workdir/training --mpl-epochs 10000 --mpl-batch-size 64 --finetune-epochs 1000 --finetune-batch-size 512

# only evaluate
python src/evaluate.py --data-dir data/cifar10 --config-name cifar10 --saved-model-dir [workdir/training/finetune/model, saved_models/mpl, saved_models/finetune]
Tip!

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Unofficial TF2 implementation of "Meta Pseudo Labels" with linked metrics, dataset and models

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