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MVREC: Few-Shot Defect Multi-Classification Framework

This repository contains the implementation of MVREC, a general framework for Few-Shot Defect Multi-Classification (FSDMC), as described in our paper accepted to AAAI 2025.

Paper: https://arxiv.org/abs/2412.16897

Overview

Few-shot defect multi-classification is a critical problem in industrial quality control. Existing works predominantly focus on anomaly detection (localization), while MVREC is the first general few-shot defect classification framework based on the CLIP paradigm. MVREC addresses these challenges with the following innovations:

  1. General Features Extraction: Leveraging the pre-trained AlphaCLIP model for defect instance feature extraction.
  2. Region-Context Enhancement: A novel framework combining mask region input and multi-view context augmentation to refine defect features.
  3. Few-shot Zip-Adapter(-F) Classifiers: Efficiently cache visual features of the support set to enable few-shot classification.
  4. New Benchmark: Introduction of MVTec-FS, a few-shot defect dataset derived from MVTec AD, featuring 1228 images, 46 defect types, and instance-level mask annotations.

MVREC

Features

  • Few-shot Support: Supports few-shot learning with k-shot configuration.
  • Multi-View Contextual Learning: Incorporates multi-view augmentation to boost classification performance.
  • Customizable: Configurable for different datasets and training scenarios.

MVTec-FS Dataset

The MVTec-FS dataset is based on MVTec AD and includes:

  • 1228 defect images
  • 46 defect types
  • Instance-level mask annotations

For more details on MVTec-FS, refer to the paper.

Download Dataset

You can download the MVTec-FS dataset from MVTec-FS GitHub repository. Once downloaded, update the data_param.py file to set the correct local paths to the dataset.

For more details on MVTec-FS, refer to the paper.

Getting Started

Prerequisites

Install the dependencies:

pip install -r requirements.txt

Repository Structure

  • data_param.py: Contains dataset configurations for MVTec-FS and other datasets.
  • fewshot_process.py: Functions for generating few-shot support/query datasets and evaluation processes.
  • param_space.py: Parameter configurations for experiments.
  • run.py: Main entry point for training and evaluation.
  • run.sh: Script for running experiments with different configurations.

Usage

Training and Evaluation

Run the framework with:

python run.py --data_option mvtec_bottle_data \
              --ClipModel.classifier EchoClassfierF \
              --ClipModel.backbone_name ViT-L/14 \
              --ClipModel.clip_name AlphaClip \
              --debug.k_shot 5 \
              --data.input_shape 224 \
              --data.mv_method mso \
              --debug.acti_beta 1 \
              --exp_name experiment_name \
              --run_name run_name

Alternatively, use the provided shell script:

bash run.sh

Configurations

  • Datasets: Defined in data_param.py. Examples include mvtec_carpet_data, mvtec_bottle_data, etc.
  • Parameters: Adjust parameters in param_space.py for different experiment settings.

Citation

If you use this repository or the MVTec-FS dataset, please cite our work:

@article{author2025mvrec,
  title={MVREC: A General Framework for Few-Shot Defect Multi-Classification},
  author={Your Name and Others},
  journal={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2025}
}

License

This project is licensed under the MIT License.

Acknowledgements

We thank the contributors to the MVTec AD dataset and the creators of AlphaCLIP.