Awesome
Datasets
Follow DATASET.md to install ImageNet and other 10 datasets referring to CoOp.
Running
You can modify the configurations in the configs/[dataset].yaml, including shots, learning rate, train epoch, etc. Here we provide the implementation of KCL on six transfer learning models including CLIP, CoOp, CLIPAdapter, Tip-Adapter, Tip-Adapter-F and MaPLe.
You can get the performance of the model without KCL by:
CUDA_VISIBLE_DEVICES=0 python main.py --config configs/[dataset].yaml --model=[model] --shots=k
For example,
CUDA_VISIBLE_DEVICES=0 python main.py --config configs/imagenet.yaml --model=CoOp --shots=1
You can run KCL by:
CUDA_VISIBLE_DEVICES=0 python main.py --config configs/[dataset].yaml --model=KCL[model] --shots=k
For example,
CUDA_VISIBLE_DEVICES=0 python main.py --config configs/imagenet.yaml --model=KCLCoOp --shots=1
Contact
Please contact zhouqf@smail.nju.edu.cn if you have any question about this project