Source code for "De-Biased Representation Learning for Long-tailed PEFT in Remote Sensing".

Clone the repository

git clone https://github.com/doem97/deblora.git

The folder structure should be like:

./
|-- data
|-- exp
|-- output
|-- scripts
|-- src
|-- .gitignore
`-- README.md

Please download datasets with huggingface-cli script and symbol link to ./data folder. See Datasets Documentation for more details.

To set up the deblora environment, follow these steps:

1. Create a new conda environment (recommended):

   conda create -n deblora python=3.8
   conda activate deblora
   

2. Install the required packages using pip:

   pip install -r requirements.txt
   

Feature extraction for 0 Shot, Fine-tuned, and LoRA:

# feature extraction for 0 shot and fine-tuned
bash ./exp/extract_0shot_feat.sh
bash ./exp/extract_finetune_feat.sh
# feature extraction for LoRA (feature calibration source for pLoRA)
bash ./exp/extract_lora_feat.sh

For easy re-produce, we also provided the ready-to-use extracted features (download links in hf_features.sh). You could directly download the 0shot/fine-tuned/LoRA/pLoRA features by executing the script.

Linear probing for 0 Shot:

bash ./exp/0shot_linprob.sh

Linear probing for Fine-tuned:

bash ./exp/ft_linprob.sh

Linear probing for LoRA:

bash ./exp/lora_linprob.sh

Feature clustering and calibration for pLoRA:

bash ./exp/feat_cluster_lora_kmeans.sh

Linear probing for pLoRA:

bash ./exp/plora_linprob.sh

Please note that the current code may be poorly organized and is in separate modules. The remaining CLIP code and data will be released in our upcoming work Meta-LoRA (https://github.com/doem97/metalora) with more streamlined pipelines. Please do not hesitate to raise repo issues if you have problems.