CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset

Mila - Quebec AI Institute & Institut de recherche d'Hydro-Québec

Akshatha Arodi*, Margaux Luck*, Jean-Luc Bedwani, Aldo Zaimi, Ge Li, Nicolas Pouliot, Julien Beaudry, Gaétan Marceau Caron

*Denotes equal contribution

[Project page]

The repository has the following structure.

cableinspect-ad-code/
├── dataset/                                    # Code to preprocess dataset
│   ├── README.md                               # Documentation of the data and preprocessing
│   ├── generate_labels.py                      # Generate labels
│   ├── generate_masks.py                       # Generate masks
│   ├── tools/local
│   │   ├── dataset.sh                          # Script to generate labels and masks
│   │   └── ...
│   └── ...
├── scripts/                                    # Scripts to run all the VLMs
│   ├── get_kfold_metrics.py                    # Generate threshold-dependant metrics
│   ├── prompts.yaml                            # Prompt for the VLMs
│   ├── winclip_ad.py                           # Script to run WinCLIP
│   ├── evaluate.ipynb                          # Notebook to generate threshold-independant metrics
│   ├── cogvlm_ad.py                            # Inference script for CogVLM
│   ├── llava13b_ad.py                          # Inference script for LLaVA-13B
│   └── ...
├── src/
│   ├── anomaly_detector/                       # Code for VLMs and WinCLIP
│   │   ├── cogvlm_ad_inference.py              # Script to run CogVLM
│   │   ├── llava_ad_inference.py               # Script to run LLaVA
│   │   └── ...
│   ├── enhanced-patchcore/                     # Code for Enhanced-PatchCore
│   │   ├── README.md                           # Documentation of Enhanced-PatchCore
│   │   ├── notebooks/                          # Notebooks for data visualization and results
│   │   ├── post_processing/                    # Script for postprocessing
│   │   ├── experiments/tools/
│   │   │   └── hq_patchcore_kfold_kshot.sh     # Bash script for running the model
│   │   └── ...
├── README.md                                   # This README file
└── ...

Table of Contents

• Dataset
• Enhanced-PatchCore
• Vision-Language Models
  • Installation
  • Usage
• WinCLIP
  • Installation
  • Usage
• Results

Dataset

We provide code for generating labels and masks. After downloading the images and annotation files, follow the instructions in the dataset README.

Enhanced-PatchCore

Instructions for installation and usage are provided in the Enhanced-PatchCore README. We also provide notebooks for results and dataset visualization.

Vision-Language Models

We provide inference scripts to evaluate all the Vision-Language Models (VLMs) reported in the paper. We also include WinCLIP in our evaluation and provide inference scripts.

Installation

To setup the environment:

conda create -n ad_env python=3.10
conda activate ad_env

We need to set these environment variables before installing torch.

envPath=$(conda info --envs | grep ad_env | awk '{print $NF}')
export CUDA_HOME=$envPath

Install cudatoolkit

conda install nvidia/label/cuda-12.0.0::cuda-toolkit

To verify the installation, run the following:

nvcc --version

Install the dependancies:

pip install -r requirements.txt

Then install the package:

pip install -e .

Pytest

pip install pytest
pytest tests/

Usage

To perform inference with a VLM

python scripts/cogvlm_ad.py --data-path DATA_PATH --test-csv labels.csv --batch-size 4 --out-csv cables_cogvlm_zero_shot_inference.csv

To compute the kfold threshold-dependent metrics (F1 Score, FPR, Precision, Recall) of a VLM from its raw inference csv output

python scripts/get_kfold_metrics.py --vlm-csv PATH_TO_VLM_INFERENCE_OUTPUT --kfold-dir DATA_PATH/k_fold_labels --output-csv-filename cables_vlm_kfold_metrics.csv

To calculate Anomaly Score (VQAScore)

python scripts/cogvlm_ad.py --data-path DATA_PATH --test-csv labels.csv --batch-size 4 --out-csv cables_cogvlm_zero_shot_vqascore.csv --generate-scores True

WinCLIP

We evaluate WinCLIP on detection and segmentation tasks and generate threshold-independant metrics.

WinCLIP Installation

We install the latest version of the anomalib library to evaluate WinCLIP.

To setup the environment:

conda create -n winclip_env python=3.10
conda activate winclip_env
pip install anomalib
anomalib install

WinCLIP Usage

Generate anomaly scores from WinCLIP using the script.

export DATASET_PATH=$HOME/CableInspect-AD
export RESULTS=$HOME/results
python scripts/winclip_ad.py --dataset-path $DATASET_PATH --output-path $RESULTS

Evaluation

The metrics can be generated using the scripts/evaluate.ipynb notebook for all the VLMs and WinCLIP.

To generate the AUPRO metric, we follow the method here

Results

Performance Metrics at Image-Level

Mean and standard deviation are calculated across all cables after averaging over all folds. VLMs and WinCLIP are evaluated in a zero-shot setting, while Enhanced-PatchCore is evaluated in a 100-shot setting using the beta-prime-95 thresholding strategy. Thresholded-metrics are not reported for WinCLIP since it necessitates a validation set.

Model	F1 Score ↑	FPR ↓	AUPR ↑	AUROC ↑
LLaVA 1.5-7B	0.59 ± 0.07	0.32 ± 0.19	0.75 ± 0.05	0.68 ± 0.04
LLaVA 1.5-13B	0.69 ± 0.02	0.66 ± 0.21	0.74 ± 0.04	0.66 ± 0.03
BakLLaVA-7B	0.69 ± 0.02	0.53 ± 0.19	0.77 ± 0.04	0.71 ± 0.03
CogVLM-17B	0.77 ± 0.02	0.34 ± 0.21	0.83 ± 0.03	0.79 ± 0.04
CogVLM2-19B	0.66 ± 0.04	0.04 ± 0.01	0.91 ± 0.02	0.86 ± 0.03
WinCLIP	-	-	0.76 ± 0.06	0.70 ± 0.04
Enhanced-PatchCore	0.75 ± 0.03	0.55 ± 0.19	0.84 ± 0.06	0.78 ± 0.05