TailCalibX : Feature Generation for Long-tail Classification

by Rahul Vigneswaran, Marc T. Law, Vineeth N. Balasubramanian, Makarand Tapaswi

[arXiv] [Code] [pip Package] [Video]

Table of contents

• 🐣 Easy Usage (Recommended way to use our method)
  • 💻 Installation
  • 👨‍💻 Example Code
• 🧪 Advanced Usage
  • ✔ Things to do before you run the code from this repo
  • 📀 How to use?
  • 📚 How to create the mini-ImageNet-LT dataset?
  • ⚙ Arguments
• 🏋️‍♂️ Trained weights
• 🪀 Results on a Toy Dataset
• 🌴 Directory Tree
• 📃 Citation
• 👁 Contributing
• ❤ About me
• ✨ Extras
• 📝 License

🐣 Easy Usage (Recommended way to use our method)

⚠ Caution: TailCalibX is just TailCalib employed multiple times. Specifically, we generate a set of features once every epoch and use them to train the classifier. In order to mimic that, three things must be done at every epoch in the following order:

1. Collect all the features from your dataloader.
2. Use the tailcalib package to make the features balanced by generating samples.
3. Train the classifier.
4. Repeat.

💻 Installation

Use the package manager pip to install tailcalib.

pip install tailcalib

👨‍💻 Example Code

Check the instruction here for a much more detailed python package information.

# Import
from tailcalib import tailcalib

# Initialize
a = tailcalib(base_engine="numpy")   # Options: "numpy", "pytorch"

# Imbalanced random fake data
import numpy as np
X = np.random.rand(200,100)
y = np.random.randint(0,10, (200,))

# Balancing the data using "tailcalib"
feat, lab, gen = a.generate(X=X, y=y)

# Output comparison
print(f"Before: {np.unique(y, return_counts=True)}")
print(f"After: {np.unique(lab, return_counts=True)}")

🧪 Advanced Usage

✔ Things to do before you run the code from this repo

• Change the data_root for your dataset in main.py.
• If you are using wandb logging (Weights & Biases), make sure to change the wandb.init in main.py accordingly.

📀 How to use?

• For just the methods proposed in this paper :
  • For CIFAR100-LT: run_TailCalibX_CIFAR100-LT.sh
  • For mini-ImageNet-LT : run_TailCalibX_mini-ImageNet-LT.sh
• For all the results show in the paper :
  • For CIFAR100-LT: run_all_CIFAR100-LT.sh
  • For mini-ImageNet-LT : run_all_mini-ImageNet-LT.sh

📚 How to create the mini-ImageNet-LT dataset?

Check Notebooks/Create_mini-ImageNet-LT.ipynb for the script that generates the mini-ImageNet-LT dataset with varying imbalance ratios and train-test-val splits.

⚙ Arguments

• --seed : Select seed for fixing it.

  • Default : 1

• --gpu : Select the GPUs to be used.

  • Default : "0,1,2,3"

• --experiment: Experiment number (Check 'libs/utils/experiment_maker.py').

  • Default : 0.1

• --dataset : Dataset number.

  • Choices : 0 - CIFAR100, 1 - mini-imagenet
  • Default : 0

• --imbalance : Select Imbalance factor.

  • Choices : 0: 1, 1: 100, 2: 50, 3: 10
  • Default : 1

• --type_of_val : Choose which dataset split to use.

  • Choices: "vt": val_from_test, "vtr": val_from_train, "vit": val_is_test
  • Default : "vit"

• --cv1 to --cv9 : Custom variable to use in experiments - purpose changes according to the experiment.

  • Default : "1"

• --train : Run training sequence

  • Default : False

• --generate : Run generation sequence

  • Default : False

• --retraining : Run retraining sequence

  • Default : False

• --resume : Will resume from the 'latest_model_checkpoint.pth' and wandb if applicable.

  • Default : False

• --save_features : Collect feature representations.

  • Default : False

• --save_features_phase : Dataset split of representations to collect.

  • Choices : "train", "val", "test"
  • Default : "train"

• --config : If you have a yaml file with appropriate config, provide the path here. Will override the 'experiment_maker'.

  • Default : None

🏋️‍♂️ Trained weights

🪀 Results on a Toy Dataset

The higher the Imb ratio, the more imbalanced the dataset is. Imb ratio = maximum_sample_count / minimum_sample_count.

Check this notebook to play with the toy example from which the plot below was generated.

🌴 Directory Tree

TailCalibX
├── libs
│   ├── core
│   │   ├── ce.py
│   │   ├── core_base.py
│   │   ├── ecbd.py
│   │   ├── modals.py
│   │   ├── TailCalib.py
│   │   └── TailCalibX.py
│   ├── data
│   │   ├── dataloader.py
│   │   ├── ImbalanceCIFAR.py
│   │   └── mini-imagenet
│   │       ├── 0.01_test.txt
│   │       ├── 0.01_train.txt
│   │       └── 0.01_val.txt
│   ├── loss
│   │   ├── CosineDistill.py
│   │   └── SoftmaxLoss.py
│   ├── models
│   │   ├── CosineDotProductClassifier.py
│   │   ├── DotProductClassifier.py
│   │   ├── ecbd_converter.py
│   │   ├── ResNet32Feature.py
│   │   ├── ResNext50Feature.py
│   │   └── ResNextFeature.py
│   ├── samplers
│   │   └── ClassAwareSampler.py
│   └── utils
│       ├── Default_config.yaml
│       ├── experiments_maker.py
│       ├── globals.py
│       ├── logger.py
│       └── utils.py
├── LICENSE
├── main.py
├── Notebooks
│   ├── Create_mini-ImageNet-LT.ipynb
│   └── toy_example.ipynb
├── readme_assets
│   ├── method.svg
│   └── toy_example_output.svg
├── README.md
├── run_all_CIFAR100-LT.sh
├── run_all_mini-ImageNet-LT.sh
├── run_TailCalibX_CIFAR100-LT.sh
└── run_TailCalibX_mini-imagenet-LT.sh

Ignored tailcalib_pip as it is for the tailcalib pip package.

📃 Citation

@inproceedings{rahul2021tailcalibX,
    title   = {{Feature Generation for Long-tail Classification}},
    author  = {Rahul Vigneswaran and Marc T. Law and Vineeth N. Balasubramanian and Makarand Tapaswi},
    booktitle = {ICVGIP},
    year = {2021}
}

👁 Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

❤ About me

Rahul Vigneswaran

✨ Extras

🐝 Long-tail buzz : If you are interested in deep learning research which involves long-tailed / imbalanced dataset, take a look at Long-tail buzz to learn about the recent trending papers in this field.

📝 License

MIT