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bipulsardar/Anomaly_Classifier

AI model by bipulsardar

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A Mixture of Experts (MoE) image classification system that identifies industrial product categories and detects anomalies using a product classification router and 12 specialized ResNet-18 experts. Hugging Face Model: https://huggingface.co/bipulsardar/AnomalyClassifier Create the following project structure: Model weights are downloaded automatically from Hugging Face when the application starts. Create a virtual environment and install the dependencies. Create requirements.txt: For GPU acceleration, install the PyTorch build compatible with your CUDA environment using the official selector: https://pytorch.org/get-started/locally/ Create app.py with the following code. The predictions will be saved to results/predictions.csv. If the dataset is extracted to /content/VisA in Google Colab: On Windows: The benchmark outputs include per-image predictions and aggregate metrics. Run these cells in order. Upload app.py to Colab or create it using a notebook cell, then run: Benchmark all VisA images: If the repository is private,…

bipulsardar/Anomaly_Classifier on Hugging Face A short preview, not the full document.

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  1. VisA Anomaly Classifier: Complete Runnable Setup
  2. 1. Project Structure
  3. 2. Requirements
  4. 3. Install Dependencies
  5. Windows
  6. Linux / macOS / Google Colab
  7. 4. Complete Application
  8. 5. Run Predictions
  9. Single image
  10. Batch image directory
  11. Evaluate the VisA dataset
  12. 6. Use in Google Colab

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Model overview

bipulsardar/Anomaly_Classifier is a task-unspecified model repository published by bipulsardar on Hugging Face. A library was not reported.

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Recent downloads (30 days)
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License
apache-2.0
Access
Not gated by Hugging Face
Created
2026-10-15T06:17:36.000Z
Last modified
2026-10-15T06:17:36.000Z

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Source: Hugging Face Hub. Metadata observed 2026-10-15T06:42:18.893Z. Daily imports are snapshots, not real-time monitoring.

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