yrt1026 / inctrl

Official implementation of CVPR'24 paper 'Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts'.

InCTRL (CVPR 2024)

Official PyTorch implementation of "Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts".

Setup

• python >= 3.10.11
• torch >= 1.13.0
• torchvision >= 0.14.0
• scipy >= 1.10.1
• scikit-image >= 0.21.0
• numpy >= 1.24.3
• tqdm >= 4.64.0

Device

Single NVIDIA GeForce RTX 3090

Run

Step 1. Download the Anomaly Detection Datasets

Download the Anomaly Detection Dataset and convert it to MVTec AD format. (The convert script.)

The dataset folder structure should look like:

DATA_PATH/
    subset_1/
        train/
            good/
        test/
            good/
            defect_class_1/
            defect_class_2/
            defect_class_3/
            ...
    ...

Step 2. Generate Training/Test Json Files of Datasets.(The generate script.)

The json folder structure should look like:

JSON_PATH/
    dataset_1/
        subset_1/
            subset_1_train_normal.json
            subset_1_train_outlier.json
            subset_1_val_normal.json
            subset_1_val_outlier.json
        subset_2/
        subset_3/
        ...
    ...

Step 3. Download the Few-shot Normal Samples for Inference on Google Drive

Step 4. Download the Pre-train Models on Google Drive

Step 5. Quick Start

Change the TEST.CHECKPOINT_FILE_PATH in config to the path of pre-train model. and run

python test.py --val_normal_json_path $normal-json-files-for-testing --val_outlier_json_path $abnormal-json-files-for-testing --category $dataset-class-name --few_shot_dir $path-to-few-shot-samples

For example, if run on the category candle of visa with k=2:

python test.py --val_normal_json_path /AD_json/visa/candle_val_normal.json --val_outlier_json_path /AD_json/visa/candle_val_outlier.json --category candle --few_shot_dir /fs_samples/visa/2/

Training

python main.py --normal_json_path $normal-json-files-for-training --outlier_json_path $abnormal-json-files-for-training --val_normal_json_path $normal-json-files-for-testing --val_outlier_json_path $abnormal-json-files-for-testing

Implementation of WinCLIP

WinCLIP is one main competing method to ours, but its official implentation is not publicly available. We have successfully reproduced the results of WinCLIP based on our extensive communications with its authors and used our implementation to perform experiments in the paper. Our implementation has been released at WinCLIP.

Citation

@inproceedings{zhu2024toward,
  title={Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts},
  author={Zhu, Jiawen and Pang, Guansong},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  year={2024}
}