This is the PyTorch implementation of "AMPL: An Adaptive Meta-Prompt Learner for Few-Shot Image Classification".

• The code will be updated soon.

Python 3.8, Pytorch 1.11, CUDA 11.3. The code is tested on Ubuntu 20.04.

We have prepared a conda YAML file which contains all the python dependencies.

conda env create -f environment.yml

To activate this conda environment,

conda activate ampl

We use wandb to log the training stats (optional).

• 𝒎𝒊𝒏𝒊ImageNet

  The 𝑚𝑖𝑛𝑖ImageNet dataset was proposed by Vinyals et al. for few-shot learning evaluation. Its complexity is high due to the use of ImageNet images but requires fewer resources and infrastructure than running on the full ImageNet dataset. In total, there are 100 classes with 600 samples of color images per class. These 100 classes are divided into 64, 16, and 20 classes respectively for sampling tasks for meta-training, meta-validation, and meta-test. To generate this dataset from ImageNet, you may use the repository 𝑚𝑖𝑛𝑖ImageNet tools.

• 𝒕𝒊𝒆𝒓𝒆𝒅ImageNet

  The 𝑡𝑖𝑒𝑟𝑒𝑑ImageNet dataset is a larger subset of ILSVRC-12 with 608 classes (779,165 images) grouped into 34 higher-level nodes in the ImageNet human-curated hierarchy. To generate this dataset from ImageNet, you may use the repository 𝑡𝑖𝑒𝑟𝑒𝑑ImageNet dataset: 𝑡𝑖𝑒𝑟𝑒𝑑ImageNet tools.

• CIFAR-FS and FC100

  CIFAR-FS and FC100 can be download using the scripts from DeepEMD.

Our model are trained on 8 RTX3090 GPUs by default (24GB memory). You can specify the argument --nproc_per_node in the following command file as the number of GPUs available in your server, and increase/decrease the argument --batch_size_per_gpu if your GPU has more/less memory.

• Pre-training (self-supervised)

  In this phase, we pretrain our model using the self-supervised learning method iBOT and SMKD (use the ). All models are trained for a maximum of 1600 epochs. We evaluate our model on the validation set after training for every 50 epochs, and report the best. 1-shot and 5-shot evaluation results with Prototype method is given in the following table. We also provide full checkpoints and test-set features for pretrained models, and command to replicate the results.

  --data_path: need to be set as the location of the training set of dataset XXX (e.g. miniImageNet). --output_dir: location where the phase1 checkpoints and evaluation files to be stored.

  Dataset	1-shot	5-shot	Download
  𝒎𝒊𝒏𝒊ImageNet	60.93%	80.38%	checkpoint	features	command
  𝒕𝒊𝒆𝒓𝒆𝒅ImageNet	71.36%	83.28%	checkpoint	features	command
  CIFAR-FS	65.70%	83.45%	checkpoint	features	command
  FC100	44.20%	61.64%	checkpoint	features	command

• Aaptive Meta-Prompt Learner (supervised)

  In this second phase, we start from the checkpoint in phase 1 and further train the model using the supervised knowledge distillation method proposed in our paper. All models are trained for a maximum of 150 epochs. We evaluate our model on the validation set after training for every 5 epochs, and report the best. Similarly, 1-shot and 5-shot evaluation results with Prototype method is given in the following table. We also provide checkpoints and features for pretrained models.

  --pretrained_dino_path: should be set as the same location as --output_dir in phase1. --pretrained_dino_file: which checkpoint file to resume from (e.g. checkpoint1250.pth). --output_dir: location where the phase2 checkpoints and evaluation files to be stored.

  Dataset	1-shot	5-shot	Download
  𝒎𝒊𝒏𝒊ImageNet	74.82%	88.47%	checkpoint	features	command
  𝒕𝒊𝒆𝒓𝒆𝒅ImageNet	78.98%	91.61%	checkpoint	features	command
  CIFAR-FS	78.69%	90.68%	checkpoint	features	command
  FC100	58.34%	72.25%	checkpoint	features	command

We use eval_ampl.py to evaluate a trained model. Before running the evaluation code, we need to specify the image data path in server_dict of this python file.

For example, we can use the following code to do 5-way 5-shot evaluation on the model trained in Pre-training on mini-ImageNet:

• prototype:

python eval_ampl.py --server mini --num_shots 5 --ckp_path /root/autodl-nas/FSVIT_results/MINI480_phase2 --ckpt_filename checkpoint0040.pth --output_dir /root/autodl-nas/FSVIT_results/MINI480_prototype --evaluation_method cosine --iter_num 10000

• classifier:

python eval_ampl.py --server mini --num_shots 5 --ckp_path /root/autodl-nas/FSVIT_results/MINI480_phase2 --ckpt_filename checkpoint0040.pth --output_dir /root/autodl-nas/FSVIT_results/MINI480_classifier --evaluation_method classifier --iter_num 1000

@inproceedings{,
      title={AMPL: An Adaptive Meta-Prompt Learner for Few-Shot Image Classification}, 
      author={},
      booktitle={},
      year={},
      pages={},
}