Waffling for Performance

Authors: Karsten Roth, Jae Myung Kim, Almut Sophia Koepke, Oriol Vinyals, Cordelia Schmid, Zeynep Akata

Table of Contents

Waffling for Performance

Overview

This repository contains code to replicate key experiments from our paper Waffling around for Performance: Visual Classification with Random Words and Broad Concepts. It should also provide a good starting point for any subsequent research looking to study improved (zero-shot) transfer performance of pretrained Vision Language Models (VLM), and extends the great repository associated with the original Visual Classification via Description from Large Language Models paper.

If you find this repository useful or use it as part of your research, please consider citing it.

Setting Up

Set up environment: To get started, simply set up the correct environment using environment.yaml by running

conda env create -f environment.yaml

and activate the environment via conda activate waffle.

Ensure clip is up-to-date: The above command should install all relevant libraries. If you are not able to utilize the ViT-L/14 backbone, it is like because your version of clip is not up-to-date. In this case, consider re-installing it:

pip install git+https://github.com/openai/CLIP.git

Downloading datasets: The associated datasets should download automatically with the exception of ImageNet, which should follow the default ImageNet2012-structure. We also note that auto-downloading Places365 sometimes causes some issues, and may need to be downloaded by hand.

Replicating

In this section, we will detail how to run both baseline approaches (CLIP, CLIP + GPT Descriptors), as well as WaffleCLIP and its variants. We also showcase how to generate your own descriptions and extract your own high-level concepts to extend default prompts with.

A large collection of sample runs to replicate baseline experiments are provided in replicate_key_results.sh, which will create a selection of result csv-files in a results-folder. You should be able to extract the information in a more readable fashion by simply using evaluate_results.py.

In the following part, we will provide a few more details on specific settings and how to run them.

Default Zero-Shot Visual Classification Performance of CLIP

To replicate the zero-shot classification performance of vanilla CLIP on e.g. the ImageNet1K test data, simply run

python base_main.py --savename='baselines' --dataset=imagenet --mode=clip --model_size=ViT-B/32

which will utilize the ViT-B/32 backbone for mode=clip on dataset=imagenet. Generated results are then appended to a csv-file named results/baselines.csv. To get results for multiple datasets, simply run with respective changes in --dataset. A list of available datasets is provided in waffle_tools.DATASETS.

Utilizing GPT Descriptors

To extend the zero-shot classification of vanilla CLIP with GPT-3 generated descriptions following Menon et al. 2023 on e.g. the ImageNet1K test data, simply run

python base_main.py --savename='baselines_gpt' --dataset=imagenet --mode=gpt_descriptions --model_size=ViT-B/32

Generated results are then appended to a csv-file named results/baselines_gpt.csv.

Generating your own GPT Descriptors

If you want to produce new GPT Descriptors for other datasets, simply utilize generate_descriptors.py, which is adapted from Menon et al. 2023. Ensure that you have a valid OpenAI account.

`Waffle`CLIP

To perform zero-shot classification using default WaffleCLIP, simple run

python base_main.py --savename='waffleclip' --dataset=imagenet --mode=waffle --waffle_count=15 --reps=7 --model_size=ViT-B/32

which utilizes 15 pairs comprising a random word and a random character sequence descriptor (i.e. 30 descriptors in total) for WaffleCLIP. The results are computed over 7 different random initializations, and then averaged. Mean and standard deviations are then stored in results/waffleclip.csv.

Utilizing High-Level Concept Guidance

Using high-level concept-guidance is as easy as using zero-shot vanilla CLIP. Given some high-level concept, e.g. food for the Food101 dataset, simply run

python base_main.py --savename='baselines_concept' --dataset=food101 --mode=clip --model_size=ViT-B/32 --label_before_text='A photo of a food: a '

which replaces the default prompt primer ("A photo of a ") with "A photo of a food: a ". This can similarly be applied to e.g. WaffleCLIP as shown above by also simply appending and changing the --label_before_text parameter.

Extract your own High-Level Concepts

Given a dataset with classnames, extracting of shared concepts can be simply done using generate_concepts.py, which selects a random subset and queries GPT-3 about commonalities.

Putting Everything Together

To run WaffleCLIP on top of GPT-Descriptors and with high-level concept guidance, one can simply combine the commands above and run

python base_main.py --savename='waffleclip_gpt_concepts' --dataset=food101 --mode=waffle_and_gpt --waffle_count=15 --reps=7 --model_size=ViT-B/32 --label_before_text='A photo of a food: a '

Repository Details

In this section, we quickly details the implemented CLIP and WaffleCLIP variants. Note that all of these methods, except for the notion of high-level concept guidance, are implemented in waffle_tools.py > load_gpt_descriptions().

As baseline methods (executable via --mode=<name>), we have

clip: Default vanilla CLIP.
gpt_descriptions: Extends CLIP with GPT-descriptions per class available in the folder descriptors.

For randomization studies, we have

(shared_)random_descriptions: Randomly shuffle and redistribute available descriptions to each class, either uniquely or shared between classes (shared). By default, a --randomization_budget=1 means that we use the same number of descriptors per class as is the average for GPT-3 provided descriptors.
swapped_descriptions: Randomly interchange lists of descriptions between classes.
scrambled_descriptions: For lists of descriptions per class, we randomly shuffle words and word orders.

For WaffleCLIP variants, we have

waffle: Denotes the standard WaffleCLIP setup using pairs of random word and random character sequence descriptors. Use --waffle_count for the number of pairs.
waffle_and_gpt: Extends WaffleCLIP with associated GPT-3 descriptors. Note that the same set of descriptors are used for each class, while the descriptors per class may differ. Use the same --waffle_count number as in --mode=waffle, it will be internally adapted to ensure the same number of descriptors and an equal balanced between randomized ones and GPT-3 descriptions. Note that through the subsampling, resulting random descriptor lists may slightly vary between classes.

For additional CLIP uses, we have also included

prompt_ensemble: Takes --waffle_count * 2 of prompts randomly sampled from waffle_tools.prompt_ensemble and provides a list of multiple prompts to average retrieval over instead.
context_prompt_ensemble: Extends prompt ensembling with high-level concept guidance. Note that the --label_before_text parameter still has to include the extracted high-level concepts.

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Waffling for Performance

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Waffling for Performance

Overview

Setting Up