Long-Tailed Classification Based on Coarse-Grained Leading Forest and Multi-Center Loss

This Project is based on Generalized Long-tailed Classification (GLT) Benchmarks

Install the Requirement

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###  Step by Step Installation   ##
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# 1. create and activate conda environment
conda create -n glt python=3.9
conda activate glt

# 2. install pytorch and torchvision
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch

# 3. install other packages
pip install joblib==1.2.0 randaugment==1.0.2 pyyaml==6.0 matplotlib==3.7.1 tqdm==4.65.0 scikit-learn==1.2.2 numpy==1.23

# 4. download this project
git clone https://github.com/jinyery/glt

Prepare GLT Datasets

We propose two datasets for the Generalized Long-Tailed (GLT) classification tasks: ImageNet-GLT and MSCOCO-GLT.

• For ImageNet-GLT (link), like most of the other datasets, we don't have attribute annotations, so we use feature clusters within each class to represent K ''pretext attributes''. In other words, each cluster represents a meta attribute layout for this class.
• For MSCOCO-GLT (link), we directly adopt attribute annotations from MSCOCO-Attribute to construct our dataset.

Please follow the above links to prepare the datasets.

Conduct Training and Testing

Train Models

Run the following command to train a TRAIN_TYPE model:

python main.py --cfg CONFIG_PATH.yaml --output_dir OUTPUT_PATH --require_eval --train_type TRAIN_TYPE --phase train

Test Models

Run the following command to test a baseline model:

python main.py --cfg CONFIG_PATH.yaml  --output_dir OUTPUT_PATH --require_eval --train_type TRAIN_TYPE --phase test --load_dir YOUR_CHECKPOINT.pth

Train All

Run the following command to train all models:

bash run_all.sh -d DATASET