Welcome to the future of anomaly detection in industrial settings! Today, we’re diving into AnomalyGPT, a remarkable tool that employs Large Vision-Language Models (LVLM) to identify irregularities without the need for tedious manual thresholds. Buckle up as we take you through the process of running AnomalyGPT, training your own model, and troubleshooting tips to ensure seamless operation.

1. Introduction

AnomalyGPT is the first of its kind in the realm of industrial anomaly detection. Unlike traditional IAD methods that rely heavily on predefined thresholds, AnomalyGPT leverages pre-trained models to automatically identify and localize anomalies in industrial images. With the capability to discern previously unseen items, it opens a world of possibilities for enhancing operational efficiency!

2. Running AnomalyGPT Demo

Ready to experience AnomalyGPT in action? Follow these straightforward steps to set up the demo:

2.1 Environment Installation

• Clone the repository locally:

git clone https://github.com/CASIA-IVA-Lab/AnomalyGPT.git

• Install the required packages:

pip install -r requirements.txt

2.2 Prepare ImageBind Checkpoint

Download the pre-trained ImageBind model using this link. Place the downloaded file into the designated directory.

2.3 Prepare Vicuna Checkpoint

Follow the instructions provided to prepare the pre-trained Vicuna model found here.

2.4 Prepare Delta Weights of AnomalyGPT

We initialize our model using the pre-trained parameters from PandaGPT. Download the weights and place them into the appropriate directories.

• For Vicuna-7B and Vicuna-13B, you can find the delta weights at these links:
• Vicuna-7B (512)
• Vicuna-7B (1024)
• Vicuna-13B (256)
• Vicuna-13B (400)

2.5 Deploying Demo

Once all prior steps are complete, you can run the demo locally:

bash cd .code
python web_demo.py

3. Train Your Own AnomalyGPT

Excited to take customization further? Here’s how you can train your own version of AnomalyGPT!

3.1 Data Preparation

Download the needed datasets from the following links:

• MVTec-AD dataset
• VisA dataset

3.2 Training Configurations

• Set up your training parameters according to your computational resources.

3.3 Training AnomalyGPT

To train AnomalyGPT on the MVTec-AD dataset, run the command below:

cd .code
bash ./scripts/train_mvtec.sh

The training script has multiple key arguments you need to configure based on your dataset paths and configurations.

4. Examples

Here’s what AnomalyGPT can reveal:

Concrete with crack

Crack capsule

A cut hazelnut

Troubleshooting

If you encounter issues during installation or execution, here are some quick troubleshooting ideas:

• Ensure all models are correctly downloaded and placed in their respective directories.
• Verify that your environment has all required packages installed correctly.
• Double-check paths and commands used for execution.

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At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.