EfficientAd

EfficientAd: Accurate Visual Anomaly Detection at Millisecond-Level Latencies.

The Pytorch implementation is openvinotoolkit/anomalib.

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Test Environment

GTX3080 / Windows10 22H2 / cuda11.8 / cudnn8.9.7 / TensorRT8.5.3 / OpenCV4.6

How to Run

1. training to generate weight files (efficientAD_[category].pt)

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   // Please refer to Anomalib's tutorial for details:
   // https://github.com/openvinotoolkit/anomalib?tab=readme-ov-file#-training
   

2. generate .wts from pytorch with .pt

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   cd ./datas/models/
   // copy your `.pt` file to the current directory.
   python gen_wts.py
   // a file `efficientAD_[category].wts` will be generated.
   

3. build and run

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   mkdir build
   cd build
   cmake ..
   make
   sudo ./EfficientAD-M -s [.wts] // serialize model to plan file
   sudo ./EfficientAD-M -d [.engine] [image folder] // deserialize and run inference, the images in [image folder] will be processed
   

Latency

average cost of doInference(in efficientad_detect.cpp) from second time with batch=1 under the windows environment above

	FP32
EfficientAD-M	12ms