51 lines
1.3 KiB
Markdown
51 lines
1.3 KiB
Markdown
# EfficientAd
|
|
|
|
EfficientAd: Accurate Visual Anomaly Detection at Millisecond-Level Latencies.
|
|
|
|
The Pytorch implementation is [openvinotoolkit/anomalib](https://github.com/openvinotoolkit/anomalib).
|
|
|
|
<p align="center">
|
|
<img src="https://github.com/wang-xinyu/tensorrtx/assets/15235574/061c90a7-fe59-48e0-a8d0-6bddc4296cf1">
|
|
</p>
|
|
|
|
# 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`)
|
|
|
|
```
|
|
// 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`
|
|
|
|
```
|
|
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
|
|
|
|
```
|
|
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 |
|