libcll is a Python package designed to make complementary label learning easier for real-world researchers. The package not only implements most of the popular complementary label learning strategies, including CPE, a SOTA algorithm in 2023 but also features CLCIFAR10 and CLCIFAR20 datasets, both of which collect complementary labels from humans. In addition, the package provides a unified interface for adding more strategies, datasets, and models.
- Python version >= 3.8, <= 3.12
- Pytorch version >= 1.11, <= 2.0
- Pytorch Lightning version >= 2.0
- To install
libclland develop locally:
cd libcll
pip install -e .
| Strategies | Type | Description |
|---|---|---|
| SCL | NL, EXP | Surrogate Complementary Loss with the negative log loss (NL) or with the exponential loss (EXP) |
| URE | NN, GA, TNN, TGA | Unbiased Risk Estimator whether with gradient ascent (GA) or empirical transition matrix (T) |
| FWD | None | Forward Correction |
| DM | None | Discriminative Models with Weighted Loss |
| CPE | I, F, T | Complementary Probability Estimates with different transition matrices (I, F, T) |
| MCL | MAE, EXP, LOG | Multiple Complementary Label learning with different errors (MAE, EXP, LOG) |
| Dataset | Number of Classes | Input Size | Description |
|---|---|---|---|
| MNIST | 10 | 28 x 28 | Grayscale images of handwritten digits (0 to 9). |
| FMNIST | 10 | 28 x 28 | Grayscale images of fashion items. |
| KMNIST | 10 | 28 x 28 | Grayscale images of cursive Japanese (“Kuzushiji”) characters. |
| Yeast | 10 | 8 | Features of different localization sites of protein. |
| Texture | 11 | 40 | Features of different textures. |
| Dermatology | 6 | 130 | Clinical Attributes of different diseases. |
| Control | 6 | 60 | Features of synthetically generated control charts. |
| Micro ImageNet10 | 10 | 3 x 64 x 64 | Contains images of 10 classes designed for computer vision research. |
| Micro ImageNet20 | 20 | 3 x 64 x 64 | Contains images of 20 classes designed for computer vision research. |
| CIFAR10 | 10 | 3 x 32 x 32 | Colored images of different objects. |
| CIFAR20 | 20 | 3 x 32 x 32 | Colored images of different objects. |
| CLMicro ImageNet10 | 10 | 3 x 64 x 64 | Contains images of 10 classes designed for computer vision research paired with complementary labels annotated by humans. |
| CLMicro ImageNet20 | 20 | 3 x 64 x 64 | Contains images of 20 classes designed for computer vision research paired with complementary labels annotated by humans. |
| CLCIFAR10 | 10 | 3 x 32 x 32 | Colored images of distinct objects paired with complementary labels annotated by humans. |
| CLCIFAR20 | 20 | 3 x 32 x 32 | Colored images of distinct objects paired with complementary labels annotated by humans. |
To reproduce training results with SCL-NL method on MNIST
python script/train.py \
--do_train \
--do_predict \
--strategy SCL \
--type NL \
--model MLP \
--dataset MNIST \
--lr 1e-4 \
--batch_size 256 \
--valid_type Accuracy \or
./script/train.sh SCL NL MLP mnist Accuracy
Find full tutorials for libcll.
We thank the following repos for the code sharing.