The official code of Long-tailed Prompt Tuning
Our code is based on the unofficial VPT code implemented by DongSky.
This repository will be updated continuously in the near future.
Download the original Places365 standard dataset from here, and then change the path of Places-LT in datasets.py by the current root path of places365standard.
Note that we have stored the train/val/test split of Places-LT in vtab directory (move into phase2 test directory and you will see this dir).
Here we present LPT trained on Places-LT dataset.
Note that for simplicity during experiments, I stored the whole model into storage... The final size of LPT checkpoint may be slightly larger (negligible) than standard ViT.
LPT (Places-LT): Google Drive
Set the checkpoint to the Phase2 test directory, and then execute the following commands:
CUDA_VISIBLE_DEVICES=0 python eval_phase2.py --dataset places365 --split fullYou will obtain:
epoch 1, overall: 50.07123%, many-shot: 49.26718%, medium-shot: 52.30573%, few-shot: 46.88312%- Training code
- More checkpoints
Give me some time to prepare the code QAQ.