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Small Object Few-shot Segmentation for Vision-based Industrial Inspection

This is an official PyTorch implementation of the paper Small Object Few-shot Segmentation for Vision-based Industrial Inspection.

@article{zhang2024small,
  title={Small Object Few-shot Segmentation for Vision-based Industrial Inspection},
  author={Zhang, Zilong and Niu, Chang and Zhao, Zhibin and Zhang, Xingwu and Chen, Xuefeng},
  journal={arXiv preprint arXiv:2407.21351},
  year={2024}
}

We present SOFS to solve problems that various and sufficient defects are difficult to obtain and anomaly detection cannot detect specific defects in Vision-based Industrial Inspection. SOFS can quickly adapt to unseen classes without retraining, achieving few-shot semantic segmentation (FSS) and few-shot anomaly detection (FAD). SOFS can segment the small defects conditioned on the support sets, e.g., it segments the defects with area proportions less than 0.03%. Some visualizations are shown in the figure below.

Visualizations under Open Domain

• We show the visualizations of SOFS for Severstal: Steel Defect Detection under the open domain, where SOFS is trained on VISION V1.

Installation

1. The default python version is python 3.8.
2. Follow the installation of DINO v2, such as xFormers.
3. Use the following commands:

pip install -r requirements.txt

Train and test on VISION V1 dataset or Ds spectrum

• Pretrained model prepare: please download DINO v2 ViT-B/14 distilled (without registers) pre-trained model in DINO v2.
• Dataset prepare: please download VISION V1 dataset, the corresponding reference is at here.
• Dataset prepare: please download Ds spectrum dataset, the corresponding reference is at here.
• Replace TRAIN.dataset_path and TEST.dataset_path with your own VISION V1/Ds spectrum dataset path.
• For Ds spectrum dataset, please firstly replace the file name in VISION v1 of Ds spectrum dataset with Capacitor_VISION/Ring_VISION..., then put these folders together including the name in dataset split.
• Replace TRAIN.backbone_checkpoint with the path of pre-trained DINO v2 ViT-B/14 distilled.
• Prepare an empty folder, replace DATASET.vision_data_save_path with the corresponding path.
• Then run the following code:

bash train.sh

• The model trains in each train split and test in the corresponding test split. The result is at the ./log.
• After you run the above command for the first time, replace DATASET.vision_data_save with False and replace DATASET.vision_data_load with True.

Inference

• We provide the model trained on VISION V1 and code for SOFS inference (open-domain test). You can put your own data for open-domain test.
• Please download SOFS model at here (Google Drive) and place it at "./SOFS_model.pth".

Prepare for Your Own Data

• You can refer to the data format in severstal_steel of Open_Domain_Data. The data in severstal_steel are from Severstal: Steel Defect Detection. Our training data do not contain this data, thus this is an open-domain test.
• Your own data should be organized as follows:

|-- Your Own Data (object name)
    |-- support
        |-- image
        |-- mask
    |-- query
        |-- image

• support contains image fold and mask fold, each image in mask fold contains {0, 255}, 255 indicates the target semantic. image fold in query contains the test image.

Test on your own dataset

• You should replace "severstal_steel" with your own object in DATASET.open_domain_test_object of "./method_config/Open_Domain/SOFS.yaml".
• Then run the following code:

sh test.sh

To-Do List

• Task 1: Release inference code and model (open-domain test).
• Task 2: Release training and test code for different datasets (part).
• Task 3: Release inference for a mixture of defective support samples and normal support samples.
• Task 4: Release online tools.

Acknowledgement

We acknowledge the excellent implementation from DINO v2, HDMNet.

License

The code is released under the CC BY-NC-SA 4.0 license.