This example PerceptiLabs model, shows how a residual neural network (ResNet) can be trained to detect anomalies in textile manufacturing, by learning to classify close-up images of textile fibers.

Note: This model is not compatible with PerceptiLabs 0.12 as the UI and components have changed.

The model is based on the skip connection pattern from ResNet. Like other vision backbones (VGG, ResNet, EfficientNet), it is designed for image classification. The output of the model is a probability distribution over 6 distinct classes.

The dataset is hosted publicly on Google Drive and can be downloaded here (1.2 GB).

The dataset consists of two files:

• X.npy is a NumPy array of shape [72000, 64, 64, 1], corresponding to 72,000 distinct 64x64 monochrome images.
• Y.npy is a NumPy array of shape [72000], which consists of the corresponding integer labels for each defect type.

The dataset is derived from the Textile Defect Detection dataset on Kaggle. We pre-processed the data into two NumPy arrays which can be immediately used for training in PerceptiLabs via Data components.

The Textile Defect Detection dataset has images of textiles with their corresponding labels based of different kinds of defects that may take place in an industrial setting. A randomly selected set of images from each class looks like the following:

Note: you must be running PerceptiLabs 0.10.0 or v0.11 to load this model. This model is not compatible with PerceptiLabs 0.12.

Follow the steps below to load the sample model in PerceptiLabs:

1. Download the data files (X.npy and Y.npy) from here.
2. Clone or download the sample model from GitHub.
3. On the ModelHub screen, import the sample model into PerceptiLabs. Note that this model is not compatible with PerceptiLabs 0.12 as the UI and components have changed. When prompted for the model's directory, navigate to and select the location of the model.json file.
4. Open the topmost Data component in the model, navigate to its code tab and update the call to np.load() passing in the absolute path of the X.npy data file that you downloaded in Step 1. For example:

...
matrix_DataData_Data_1_0 = np.load("c:/Textile-Classification-master/X.npy", mmap_mode='r+').astype(np.float32)
...

5. Save the code changes for that Data component.
6. Open the bottom most Data component, navigate through its menus until you get to the Choose Files button.
7. Click Choose Files, navigate to the Y.npy data file that you downloaded in Step 1, and click Confirm.

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