Code for the paper "Balanced Meta-Softmax for Long-Tailed Visual Recognition" on long-tailed visual recognition datasets.
Balanced Meta-Softmax for Long-Tailed Visual Recognition
Jiawei Ren, Cunjun Yu, Shunan Sheng, Xiao Ma, Haiyu Zhao, Shuai Yi, Hongsheng Li
NeurIPS 2020
def balanced_softmax_loss(labels, logits, sample_per_class, reduction):
"""Compute the Balanced Softmax Loss between `logits` and the ground truth `labels`.
Args:
labels: A int tensor of size [batch].
logits: A float tensor of size [batch, no_of_classes].
sample_per_class: A int tensor of size [no of classes].
reduction: string. One of "none", "mean", "sum"
Returns:
loss: A float tensor. Balanced Softmax Loss.
"""
spc = sample_per_class.type_as(logits)
spc = spc.unsqueeze(0).expand(logits.shape[0], -1)
logits = logits + spc.log()
loss = F.cross_entropy(input=logits, target=labels, reduction=reduction)
return loss
python main.py --cfg ./config/CIFAR10_LT/softmax_imba200.yaml
Alternatively, you may download a pretrained model here and put it in the corresponding log folder.
python main.py --cfg ./config/CIFAR10_LT/balanced_softmax_imba200.yaml
After obataining the base model, train the classifier with the following command:
python main.py --cfg ./config/CIFAR10_LT/decouple_balanced_softmax_imba200.yaml
python main.py --cfg ./config/CIFAR10_LT/balms_imba200.yaml
Model evaluation can be done using the following command:
python main.py --cfg ./config/CIFAR10_LT/balms_imba200.yaml --test
The results could be slightly different from the results reported in the paper, since we originally used an internal repository for the experiments in the paper.
Dataset | Backbone | Method | Accuracy | download |
---|---|---|---|---|
CIFAR-10 (IF 200) | ResNet-32 | Softmax | 74.0 | model | log |
CIFAR-10 (IF 200) | ResNet-32 | Balanced Softmax (end-to-end) | 79.8 | model | log |
CIFAR-10 (IF 200) | ResNet-32 | Balanced Softmax (decouple) | 81.8 | model | log |
CIFAR-10 (IF 200) | ResNet-32 | BALMS | 82.2 | model | log |
CIFAR-100 (IF 200) | ResNet-32 | Softmax | 41.2 | model | log |
CIFAR-100 (IF 200) | ResNet-32 | Balanced Softmax (end-to-end) | 46.7 | model | log |
CIFAR-100 (IF 200) | ResNet-32 | Balanced Softmax (decouple) | 47.2 | model | log |
CIFAR-100 (IF 200) | ResNet-32 | BALMS | 48.0 | model | log |
ImageNet-LT | ResNet-10 | Softmax | 34.8 | model | log |
ImageNet-LT | ResNet-10 | BALMS | 41.6 | model | log |
Places-LT | ResNet-152 | Softmax | 30.2 | model | log |
Places-LT | ResNet-152 | BALMS | 38.3 | model | log |
@inproceedings{
Ren2020balms,
title={Balanced Meta-Softmax for Long-Tailed Visual Recognition},
author={Jiawei Ren and Cunjun Yu and Shunan Sheng and Xiao Ma and Haiyu Zhao and Shuai Yi and Hongsheng Li},
booktitle={Proceedings of Neural Information Processing Systems(NeurIPS)},
month = {Dec},
year={2020}
}
For BALMS on instance segmentation, please try out this repo.