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DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks
Reference PatchCore anomaly detection model
Major features
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Using nominal (non-defective) example images only
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Faiss(CPU/GPU)
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TensorRT Deployment
$ git clone https://github.com/tbcvContributor/DeepHawkeye.git
$ pip install opencv-python
$ pip install scipy
# pytorch
$ pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
#install faiss
# CPU-only version（currently available on Linux, OSX, and Windows）
$ conda install -c pytorch faiss-cpu
# GPU(+CPU) version （containing both CPU and GPU indices, is available on Linux systems）
$ conda install -c pytorch faiss-gpu
# or for a specific CUDA version
$ conda install -c pytorch faiss-gpu cudatoolkit=10.2 # for CUDA 10.2 Wide ResNet-50-2 and demo data
${ROOT}
└——————weights
└——————wide_r50_2.pth
└——————demo_data
└——————grid
└——————normal_data
└——————test_data
└——————....
bulid normal lib
python demo_train.py -d ./demo_data/grid/normal_data -c grid
pytorch infer
python demo_test.py -d ./demo_data/grid/test_data -c grid
tensorrt infer
python demo_trt.py -d ./demo_data/grid/test_data -c grid -t ./weights/w_res_50.trt
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Need normal example images to cover all scenarios as much as possible
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Faiss Documentation Default IVFXX, PQ16
train args
def get_train_args():
parser = argparse.ArgumentParser()
parser.add_argument('-d','--total_img_paths',type=str, default=None)
parser.add_argument('-c','--category',type=str, default=None)
parser.add_argument('--batch_size', default=64)
parser.add_argument('--embedding_layers',choices=['1_2', '2_3'], default='2_3')
parser.add_argument('--input_size', default=(224, 224))
parser.add_argument('--weight_path', default='./weights/wide_r50_2.pth')
parser.add_argument('--normal_feature_save_path', default=f"./index_lib")
parser.add_argument('--model_device', default="cuda:0")
parser.add_argument('--max_cluster_image_num', default=1000,help='depend on CPU memory, more than total images number')
parser.add_argument('--index_build_device', default=-1,help='CPU:-1 ,GPU number eg: 0, 1, 2 (only on Linux)')
tips：
--input_size: trade off between speed and accuracy of the result --max_cluster_image_num：If RAM allows, greater than or equal to the total number of samples
test args
def get_test_args():
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--test_path', type=str, default=None)
parser.add_argument('-c', '--category', type=str, default=None)
parser.add_argument('--model_device', default="cuda:0")
parser.add_argument('--test_batch_size', default=64)
parser.add_argument('--embedding_layers', choices=['1_2', '2_3'], default='2_3')
parser.add_argument('--input_size', default=(224, 224))
parser.add_argument('--test_GPU', default=-1, help='CPU:-1,'
'GPU: num eg: 0, 1, 2'
'multi_GPUs:[0,1,...]')
parser.add_argument('--save_heat_map_image', default=True)
parser.add_argument('--heatmap_save_path',
default=fr'./results', help='heatmap save path')
parser.add_argument('--threshold', default=2)
parser.add_argument('--nprobe', default=10)
parser.add_argument('--n_neighbors', type=int, default=5)
parser.add_argument('--weight_path', default='./weights/wide_r50_2.pth')
parser.add_argument('--normal_feature_save_path', default=f"./index_lib")
tips：
--threshold: depend on scores of anomaly data
result format：{filename}_{score}.jpg
This project is released under the Apache 2.0 license.
https://github.com/hcw-00/PatchCore_anomaly_detection embedding concat function : https://github.com/xiahaifeng1995/PaDiM-Anomaly-Detection-Localization-master