DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks

Last update: Dec 13, 2022

Overview

English | 简体中文

Introduction

DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks

Reference PatchCore anomaly detection model

Major features

Using nominal (non-defective) example images only
Faiss(CPU/GPU)
TensorRT Deployment

Installation

$ 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

Checkpoints and Demo data

Wide ResNet-50-2 and demo data

[Google]

[Baidu],code:a14e

${ROOT}
   └——————weights
           └——————wide_r50_2.pth
   └——————demo_data
           └——————grid
                    └——————normal_data
                    └——————test_data
           └——————....

Demo

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

Tutorials

Need normal example images to cover all scenarios as much as possible
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

TensorRT Deployment

License

This project is released under the Apache 2.0 license.

Code Reference

https://github.com/hcw-00/PatchCore_anomaly_detection embedding concat function : https://github.com/xiahaifeng1995/PaDiM-Anomaly-Detection-Localization-master

DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks

Related tags

Overview

Introduction

Installation

Checkpoints and Demo data

Demo

Tutorials

License

Code Reference

Owner

CV Newbie

(JMLR'19) A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

MolRep: A Deep Representation Learning Library for Molecular Property Prediction

This project provides an unsupervised framework for mining and tagging quality phrases on text corpora with pretrained language models (KDD'21).

Storchastic is a PyTorch library for stochastic gradient estimation in Deep Learning

Additional environments compatible with OpenAI gym

the code of the paper: Recurrent Multi-view Alignment Network for Unsupervised Surface Registration (CVPR 2021)

The implementation our EMNLP 2021 paper "Enhanced Language Representation with Label Knowledge for Span Extraction".

[CVPR'21] Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild

A rule-based log analyzer & filter

U-Time: A Fully Convolutional Network for Time Series Segmentation

Optimizing DR with hard negatives and achieving SOTA first-stage retrieval performance on TREC DL Track (SIGIR 2021 Full Paper).

Robot Hacking Manual (RHM). From robotics to cybersecurity. Papers, notes and writeups from a journey into robot cybersecurity.

Robust Instance Segmentation through Reasoning about Multi-Object Occlusion [CVPR 2021]

[제 13회 투빅스 컨퍼런스] OK Mugle! - 장르부터 멜로디까지, Content-based Music Recommendation

3.8% and 18.3% on CIFAR-10 and CIFAR-100

Cross-platform CLI tool to generate your Github profile's stats and summary.

Fusion-in-Decoder Distilling Knowledge from Reader to Retriever for Question Answering

PyTorch implementation of our ICCV paper DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection.

ManipulaTHOR, a framework that facilitates visual manipulation of objects using a robotic arm

Proto-RL: Reinforcement Learning with Prototypical Representations