├── .gitignore ├── images ├── good.png └── defects.png ├── create_low_shot_train.py ├── README.md └── LICENSE-DATASET /.gitignore: -------------------------------------------------------------------------------- 1 | # Python 2 | __pycache__/ 3 | .idea/ 4 | -------------------------------------------------------------------------------- /images/good.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/abc-125/segad/HEAD/images/good.png -------------------------------------------------------------------------------- /images/defects.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/abc-125/segad/HEAD/images/defects.png -------------------------------------------------------------------------------- /create_low_shot_train.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import shutil 3 | import os 4 | import json 5 | 6 | 7 | def get_args(): 8 | parser = argparse.ArgumentParser() 9 | parser.add_argument("--vad_path", type=str, default="", help="Path to the VAD dataset") 10 | return parser.parse_args() 11 | 12 | 13 | if __name__ == '__main__': 14 | args = get_args() 15 | train_path = os.path.join(args.vad_path, "train/bad") 16 | low_shot_train_fn = os.path.join(args.vad_path, "low_shot_train.json") 17 | 18 | with open(low_shot_train_fn, "r") as file: 19 | low_shot_train = json.load(file) 20 | 21 | for key, filenames in low_shot_train.items(): 22 | new_path = os.path.join(args.vad_path, "low_shot_train", str(key), "bad") 23 | os.makedirs(new_path, exist_ok=True) 24 | for fn in filenames: 25 | shutil.copy(os.path.join(train_path, fn), os.path.join(new_path, fn)) 26 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Valeo Anomaly Dataset (VAD) 2 | All images in VAD are captured from an actual production line, showcasing a diverse range of defects, from highly obvious to extremely subtle. This dataset bridges the gap between the academic community and industry, offering researchers the opportunity to advance the performance of methods in tackling more complex real-world challenges. [Published](https://arxiv.org/abs/2405.04953) at CVPR 2024. 3 | 4 | ## 🚀 Get started 5 | 1. Download [VAD](https://drive.google.com/file/d/1sWNZCUnb2u4hcr6ak7gjpwsm6jtx6IF1/view?usp=sharing) 6 | 2. To get split for the low-shot benchmark, run `create_low_shot_train.py` in the dataset folder or use `--vad_path` to set the path to the dataset. 7 | 3. Data structure is similar to [MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad): 8 | 9 | ```shell 10 | vad 11 | |-- test 12 | |-----|--bad 13 | |-----|--bad_unseen_defects 14 | |-----|--good 15 | |-- train 16 | |-----|--bad 17 | |-----|--good 18 | |-- ground_truth 19 | |-----|--bad 20 | |-----|--bad_unseen_defects 21 | |-- low_shot_train.json 22 | ``` 23 | 24 | ## 💡 Now available in [Anomalib](https://github.com/open-edge-platform/anomalib) 25 | Currently, only one-class (unsupervised) version is available. 26 | 27 | ```python 28 | from anomalib.data import VAD 29 | from anomalib.models import Patchcore 30 | from anomalib.engine import Engine 31 | 32 | # Initialize components 33 | datamodule = VAD() 34 | model = Patchcore() 35 | engine = Engine() 36 | 37 | # Train the model 38 | engine.fit(datamodule=datamodule, model=model) 39 | ``` 40 | 41 | ## 📦 Data description 42 | ![](images/good.png) 43 | 44 | ![](images/defects.png) 45 | 46 | VAD consists of one class with predefined training and testing sets. The training set contains 1000 bad and 2000 good images, and the testing set contains 1000 bad images, 165 of which are unseen defects, and 1000 good images. Unseen defects in the test dataset refer to several rare defect types not present in the training data. 47 | It contains 20+ types of defects; see Appendix C of the [paper](https://arxiv.org/abs/2405.04953) for details. 48 | 49 | ## 📄 License 50 | VAD is released under [CC BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/). 51 | 52 | ## ✨ Citation 53 | Please cite this paper if you find our dataset useful: 54 | 55 | ``` 56 | @inproceedings{baitieva2024supervised, 57 | title={Supervised Anomaly Detection for Complex Industrial Images}, 58 | author={Aimira Baitieva and David Hurych and Victor Besnier and Olivier Bernard}, 59 | booktitle={CVPR}, 60 | year={2024} 61 | } 62 | ``` 63 | -------------------------------------------------------------------------------- /LICENSE-DATASET: -------------------------------------------------------------------------------- 1 | Attribution-NonCommercial 4.0 International 2 | 3 | ======================================================================= 4 | 5 | Creative Commons Corporation ("Creative Commons") is not a law firm and 6 | does not provide legal services or legal advice. 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