Long-tailed Partial Label Learning by Head Classifier and Tail Classifier Cooperation

This is a official PyTorch implementation for Long-tailed Partial Label Learning by Head Classifier and Tail Classifier Cooperation (AAAI24).

Data Preparation

CIFAR and SUN397

For CIFAR and SUN397 datasets, One can directly run shell codes and the dataset will be automatically downloaded.

PASCAL VOC 2007

Download the LT-PLL version of PASCAL VOC 2007 and extract it to "./data". This LT-PLL dataset is builded by RECORDS.

We provide the following shell codes for model training.

Run CIFAR10

python -u train.py  --dataset cifar10 --partial_rate 0.5 --imb_ratio 100 \
--exp-dir experiment/CIFAR10 --data_dir ./data \
--epochs 800 --batch-size 256 --lr 0.01 --wd 1e-3 \
--t 2 --save_ckpt

Run CIFAR100

python -u train.py  --dataset cifar100 --partial_rate 0.05 --imb_ratio 20 \
--exp-dir experiment/CIFAR100 --data_dir ./data \
--epochs 800 --batch-size 256 --lr 0.01 --wd 1e-3 \
--t 2 --save_ckpt

Run SUN397

python -u train.py  --dataset sun397 --partial_rate 0.05 --imb_ratio 1 \
--exp-dir experiment/SUN397 --data_dir ./data \
--epochs 200 --batch-size 128 --lr 0.01 --wd 1e-3 \
--t 2 --save_ckpt

Run PASCAL VOC

python -u train.py  --dataset voc --partial_rate 0 --imb_ratio 1 \
--exp-dir experiment/VOC --data_dir ./data \
--epochs 200 --batch-size 128 --lr 0.01 --wd 1e-3 \
--t 0.99 --save_ckpt