https://github.com/tootouch/balancedsoftmax

Balanced Softmax for classification
https://github.com/tootouch/balancedsoftmax

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Balanced Softmax for classification

• Host: GitHub
• URL: https://github.com/tootouch/balancedsoftmax
• Owner: TooTouch
• License: apache-2.0
• Created: 2023-08-13T12:25:33.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2023-08-18T15:11:55.000Z (almost 3 years ago)
• Last Synced: 2025-03-01T06:43:30.482Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 4.02 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# BalancedSoftmax

Balanced Softmax for classification.

This repository only considers Balanced Softmax.

- **paper**: [Balanced Meta-Softmax for Long-Tailed Visual Recognition](https://proceedings.neurips.cc/paper/2020/file/2ba61cc3a8f44143e1f2f13b2b729ab3-Paper.pdf) (NeurIPS 2020)

- **official github**: https://github.com/jiawei-ren/BalancedMetaSoftmax-Classification/tree/main

# Environment

I use a docker image. `nvcr.io/nvidia/pytorch:22.12-py3`

```bash

pip install -r requirements.txt

```

# Datasets

[datasets/build.py](https://github.com/TooTouch/BalancedSoftmax/blob/main/datasets/build.py)

- CIFAR10-LT

- CIFAR100-LT

```python

from datasets import CIFAR10LT

trainset = CIFAR10LT(

    root             = '/datasets/CIFAR10',

    train            = True,

    download         = True,

    imb_type         = 'exp',

    imbalance_factor = 200,

)

print(trainset.num_per_cls)

>> {0: 5000,

    1: 2775,

    2: 1540,

    3: 854,

    4: 474,

    5: 263,

    6: 146,

    7: 81,

    8: 45,

    9: 25}

```

# Balanced Softmax

[losses.py](https://github.com/TooTouch/BalancedSoftmax/blob/main/losses.py)

```python

from losses import BalancedSoftmax

num_per_cls = list(trainset.num_per_cls.values())

criterion = BalancedSoftmax(num_per_cls=num_per_cls)

```

# Experiments

## 1. Experiment setting

[configs.yaml](https://github.com/TooTouch/BalancedSoftmax/blob/main/configs.yaml)

```yaml

DEFAULT:

  seed: 0

  savedir: ./results

  exp_name: CE-IF_1

DATASET:

  datadir: /datasets

  batch_size: 32

  test_batch_size: 2048

  num_workers: 12

  imbalance_type: null

  imbalance_factor: 1

  aug_info:

    - RandomCrop

    - RandomHorizontalFlip

LOSS:

  name: CrossEntropyLoss

OPTIMIZER:

  name: SGD

  lr: 0.1

SCHEDULER:

  sched_name: cosine_annealing

  params:

    t_mult: 1

    eta_min: 0.00001

TRAIN:

  epochs: 50

  grad_accum_steps: 1

  mixed_precision: fp16

  log_interval: 10

  ckp_metric: bcr

  wandb:

    use: true

    entity: tootouch

    project_name: Balanced Softmax

MODEL:

  name: resnet18

  pretrained: false

```

## 2. Run

[run.sh](https://github.com/TooTouch/BalancedSoftmax/blob/main/run.sh)

```bash

dataname='CIFAR10LT CIFAR100LT'

IF='1 10 50 100 200'

losses='CrossEntropyLoss BalancedSoftmax'

for d in $dataname

do

    for f in $IF

    do

        for l in $losses

        do

            if [ $f == '1' ] && [ $l == 'BalancedSoftmax' ]; then

                continue

            else

                echo "dataset: $d, loss: $l, IF: $f"

                python main.py --config configs.yaml \

                            DEFAULT.exp_name $l-IF_$f \

                            DATASET.name $d \

                            DATASET.imbalance_type exp \

                            DATASET.imbalance_factor $f \

                            LOSS.name $l

            fi

        done

    done

done

```

## 3. Results

### 3.1 Imbalance type - exp

**Experiments log** [ [wandb](https://wandb.ai/tootouch/Balanced%20Softmax?workspace=user-tootouch) ] 



 
Figure 1. Imbalance factor에 따른 실험 결과



  

    Table 1. Imbalance factor에 따른 실험 결과

  

  

    

      Dataset

      CIFAR10LT

      CIFAR100LT

    

    

      Imbalance factor

      1

      10

      50

      100

      200

      1

      10

      50

      100

      200

    

  

  

    

      CrossEntropyLoss

      0.9283

      0.8717

      0.7779

      0.7065

      0.6426

      0.7313

      0.5865

      0.4544

      0.4060

      0.3492

    

    

      BalancedSoftmax

      

      0.8694

      0.7992

      0.7601

      0.7034

      

      0.5999

      0.4845

      0.4447

      0.3823

    

  



 
Figure 2. Imbalance factor에 따른 실험 결과 class별 성능





 
Figure 3. CIFAR10LT에 대한 cross entropy와 balanced softmax 간 confusion matrix 비교. Imbalance factor(IF)는 200.



### 3.2 Imbalance type - step



 
Figure 4. Imbalance factor에 따른 실험 결과



  

    Table 1. Imbalance factor에 따른 실험 결과

  

  

    

      Dataset

      CIFAR10LT

      CIFAR100LT

    

    

      Imbalance factor

      1

      10

      50

      100

      200

      1

      10

      50

      100

      200

    

  

  

    

      CrossEntropyLoss

      0.9283

      0.8525

      0.7078

      0.6421

      0.5570

      0.7313

      0.5696

      0.4440

      0.4067

      0.3921

    

    

      BalancedSoftmax

      

      0.8762

      0.8027

      0.7633

      0.7070

      

      0.6058

      0.5202

      0.4715

      0.4301

    

  



 
Figure 5. Imbalance factor에 따른 실험 결과 class별 성능





 
Figure 6. CIFAR10LT에 대한 cross entropy와 balanced softmax 간 confusion matrix 비교. Imbalance factor(IF)는 200.